Asthma Care Quality Improvement: A Resource Guide for State Action Prepared for: Agency for Healthcare Research and Quality U.S. Department of Health and Human Services 540 Gaither Road Rockville, Maryland 20850 www.ahrq.hhs.gov Contract No. 290-00-0004 Updated under Contract No. HHSA-290-2006-00009-C Thomson Reuters (formerly Thomson Medstat) The Council of State Governments Prepared by: Rosanna M. Coffey, Ph.D. Karen Ho, M.H.S. David M. Adamson, Ph.D. Trudi L. Matthews, M.A. Jenny Sewell Cheryl A. Kassed, Ph.D.

AHRQ Publication No. 06(10)-0012 Updated October 2009

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Acknowledgments This Resource Guide was prepared for the Agency for Healthcare Research Quality (AHRQ) by Thomson Medstat and The Council of State Governments and updated by Thomson Reuters(formerly Medstat). This effort was motivated and guided by the following AHRQ staff: Dwight McNeill, Ph.D., AHRQ Task Leader; Ernest Moy, M.D., M.P.H., Director of the National Healthcare Quality Report and Disparities Reports; Denise Dougherty, Ph.D., Special Advisor on Child Health; Roxanne Andrews, Senior Health Services Researcher, Center for Delivery, Organization, and Markets; and DonnaRae Castillo, Publications Editor, Office of Communications and Knowledge Transfer (retired). We also acknowledge the special contributions of the following individuals and groups: • Several members of AHRQ’s Asthma Steering Committee provided valuable comments and

recommendations on earlier drafts of this Resource Guide. They are: James Stout (University of Washington), Diana Schmidt (National Heart, Lung, and Blood Institute), Stephen Redd (Centers for Disease Control and Prevention), David Greenberg (Centers for Medicare & Medicaid Services), Ashok Patel (American Thoracic Society member), Maureen George (American Thoracic Society member), Kirsten Aired (Oregon Asthma Program), Amy Friedman (Allies Against Asthma), Katherine Pruitt (American Lung Association), Asua Ofosu (American Thoracic Society), Mary Tyrell (South Carolina State Budget and Control Board), Vi Naylor (Georgia Hospital Association), and Carolyn Turner (Florida Agency for Health Care Administration).

• Partners in the Healthcare Cost and Utilization Project (HCUP) from State hospital associations and State government agencies contributed data and information on the four States featured in this Resource Guide. They are: Maryland—Brian Jacque (Health Services Cost Review Commission, Department of Research and Methodology); Michigan—Bob Zorn (Michigan Health & Hospital Association); New Jersey—Frances Prestianni (New Jersey Department of Health & Senior Services); Vermont—Lauri Scharf (Vermont Association of Hospitals and Health Systems). All HCUP Partners make possible the HCUP databases and, thus, derived estimates for asthma hospitalizations published in the National Healthcare Quality and Disparities Reports and this Resource Guide.

• A focus group of legislators and program directors, who are members of the Council of State Governments, guided our effort to make materials more user friendly to government executives. They are: Representative Marilyn Lee (Hawaii), Representative Kathy Miles (South Dakota), Representative Jean Hunhoff (South Dakota), and DeeAnne Mansfield (Kentucky Legislative Research Commission).

_____________________________________________________________________________________ This document is in the public domain within the United States only and may be used and reproduced without permission. AHRQ appreciates citation as to source and the suggested format is below. Foreign countries and users who want to distribute content on a global basis in electronic form or print should submit specific permission requests for use to: info@ahrq.gov.

Coffey RM, Ho K, Adamson DM, Matthews TL, Sewell J, Kassed CA. Asthma Care Quality Improvement: A Resource Guide for State Action. (Prepared by Thomson Medstat and The Council of State Governments under Contract No. 290-00-0004; updated by Thomson Reuters, formerly Thomson Medstat). Rockville, MD: Agency for Healthcare Research and Quality, Department of Health and Human Services; April 2006, updated October 2009. AHRQ Pub. No. 06(10)-0012.

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Foreword Asthma Care Quality Improvement: A Resource Guide for State Action and its accompanying Workbook were developed by Thomson Medstat and The Council of State Governments for the Agency for Healthcare Research and Quality (AHRQ) as learning tools for all State officials who want to improve the quality of health care for people with asthma in their States. Using State-level data on asthma care, this Resource Guide is designed to help States assess the quality of care in their States and fashion quality improvement strategies suited to State conditions. The States mentioned in this Resource Guide gave permission to use their data for illustrative and comparative purposes so that others could learn by their examples. Many people for whom these learning tools were intended—State elected and appointed leaders as well as officials in State health departments, Asthma Prevention and Control Programs, Medicaid offices, and elsewhere—provided comments and feedback throughout the development and drafting process. From this process, we learned that they intend to use the Resource Guide and Workbook in many different ways: to assess their current structure and status, to create new quality improvement programs, to build on existing programs, to orient new staff, and to share with their partners such as the American Lung Association. The Resource Guide and its complementary Workbook can serve as tools for those who work on quality improvement to use in sharing their expertise, ideas, knowledge, and solutions. The various modules are intended for different users. Senior leaders, for example, may want to focus on making the case for asthma quality improvement, incorporating a State-led framework into their improvement strategy, and taking action; program staff need to provide the measures and data necessary to implement the quality improvement plan. The goal is that everyone work as a team and, thereby, improve the quality of asthma care in their State. If you have any comments or questions on this Resource Guide, please contact AHRQ’s Center for Quality Improvement and Patient Safety, 540 Gaither Road, Rockville, MD 20850 (http://info.ahrq.gov).

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Asthma Steering Committee The following experts in asthma care and State health policy were assembled by AHRQ to guide the development of this Resource Guide. Kirsten Aired Oregon Asthma Program Michelle Cloutier, MD Connecticut Children’s Medical Center Amy Friedman Allies Against Asthma Min Gayles. MPH National Committee for Quality Assurance Maureen George, PhD, RN American Thoracic Society member Foster Gesten, MD New York State Department of Health David Greenberg, MBA Centers for Medicare and Medicaid Services Jayne Jones, MPH Pennsylvania Health Care Cost Containment Council Carole Lannon, MD, MPH North Carolina Center for Children’s Healthcare Improvement Vi Naylor Georgia Hospital Association Asua Ofosu American Thoracic Society Ashok Patel, MD American Thoracic Society member

Katherine Pruitt American Lung Association Stephen Redd, MD Centers for Disease Control and Prevention Joyce Reid Georgia Hospital Association Research & Education Foundation Diana Schmidt, MPH National Heart, Lung, and Blood Institute Sharon Sprenger, MPA, RHIA Joint Commission on Accreditation of Healthcare Organizations James Stout, MD, MPH University of Washington Carolyn Turner Florida Agency for Health Care Administration Mary Tyrell South Carolina State Budget and Control Board Kevin Weiss, MD, MPH, FACP Midwest Center for Health Services and Policy Research and Center for Healthcare Studies at Feinberg School of Medicine

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Executive Summary Health care in America is plagued by extensive gaps in quality. Too often care provided to patients does not match what the medical community has determined to be the most effective care. Abundant research has shown that these gaps in quality are responsible for increased costs, wasteful and ineffective care, preventable complications, avoidable hospitalizations, decreased quality of life, disability, and premature death. The National Healthcare Quality Report (NHQR) and National Healthcare Disparities Report (NHDR), published annually by the Agency for Healthcare Research and Quality since 2003, provide both extensive research and data on the extent of health care quality gaps as well as national benchmarks for quality. This Resource Guide and its accompanying Workbook draw on the NHQR and the NHDR to support State-level efforts to improve the quality of asthma care. This Resource Guide is designed to help State leaders identify measures of asthma care quality, assemble data on asthma care, assess areas of care most in need of improvement, and learn what other States have done to improve asthma care. Taken together, the Resource Guide and its companion Workbook can help State leaders to develop an asthma quality improvement action strategy.

Why Asthma? Asthma is a chronic lung condition that impairs normal breathing. The disease affects a growing number of Americans. In 2007, 16.2 million adults and 6.7 million children stated they currently had asthma (Pleis and Lucas, 2009). Asthma is also costly: total estimated costs in 2007 were $19.7 billion (American Lung Association, 2009). For several reasons, asthma presents an opportune target for quality improvement: • Increased prevalence, especially among children and adolescents • Disparities between socioeconomic groups and between racial/ethnic groups in terms of

diagnoses and quality of asthma care • A range of interventions and treatment that can successfully control the disease and prevent

attacks • High health care costs of uncontrolled asthma and the potential for a positive return on

investment for purchasers and the health care system as a whole through asthma quality improvement.

Improved quality of asthma care may help to cut costs, reduce disparities, and improve the quality of life for millions of people with asthma.

A State-Led Framework for Improving Asthma Quality of Care The Resource Guide introduces a framework for improving health care quality at the State level. States have typically viewed their role in quality improvement from a public health perspective

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or, more narrowly, as a buyer of health insurance for State employees. However, States can play a more comprehensive leadership role. Some States are already doing this, at least in part, with respect to asthma. This approach envisions three central roles for States in quality improvement: • Provide leadership, which entails providing a defining vision for change, setting goals, and

providing an environment that fosters improvement. • Work in partnership, which involves creating a committed partnership of stakeholders

dedicated to identifying, proposing, and testing solutions and developing plans for improvement.

• Implement improvement, which means implementing changes, measuring and analyzing the results of changes, and applying successful improvements on a broader scale.

Learning From Current State Quality Improvement Efforts Many States have already begun programs or demonstrations to improve the quality of asthma care. These actions can inform broader efforts within the State or the efforts of other States. This Resource Guide identifies a broad range of current asthma quality improvement activities, including public-private coalitions, cross-agency initiatives, data measurement and reporting projects, disease management training, and educational outreach programs for minority and rural populations.

Measuring the Quality of Asthma Care Assessing State quality of care for asthma requires good data and useful measures. Useful quality measures include process measures, which reflect the quality of care delivered, and outcome measures, which reflect patient health status. The former can guide health care providers on how to change while the latter can gauge whether the changed processes have had the intended effect. The NHQR provides a starting point for accessing consensus-based measures. The NHQR provides estimates for asthma hospitalizations by State. In addition, this Resource Guide incorporates estimates from the Behavioral Risk Factor Surveillance System to assess asthma care quality by State. Although a consensus on a few key measures of asthma care quality has not yet evolved, an inventory of the many measures available is provided. Data are also essential to improve quality. States need performance data on asthma care to assess their own performance against national benchmarks and to focus quality improvement efforts by identifying potential problem areas. A list of national, State, and local sources for estimates for asthma, asthma care, and other related information is also included.

Moving Ahead: Implications for State Action Identifying measures and data sources is only a first step. As part of a systematic initiative to improve the quality of asthma care, States will need to bundle these resources into a comprehensive, State-specific picture of asthma care that identifies areas for improvement and provides a basis for planning among the partners. This picture may require collecting specific data on asthma care that focus on a State’s health care systems. Doing so will enable States to identify specific quality problems in their own communities, tailor specific solutions, and assess the effectiveness of specific interventions

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Contents Page

Executive Summary .......................................................................................................................v

Introduction ..................................................................................................................................1

Module 1: Making the Case for Asthma Care Quality Improvement .....................................5

The Need for Asthma Care Quality Improvement..........................................................................5

The Quality Improvement Opportunity .........................................................................................15

Estimating the Costs of Asthma Care and Potential Savings From Quality Improvement ...........18

Module 2: A Framework for State-Led Quality Improvement ..............................................26

Quality Health Care and the Quality Improvement Movement.....................................................26

A Strategic Role for States.............................................................................................................27

Developing a Framework for State-Led Quality Improvement .....................................................27

Information Resources for Quality Improvement..........................................................................35

Module 3: Learning From Current State Quality Improvement Efforts ..............................39

Current State Efforts To Improve the Quality of Asthma Care .....................................................39

Module 4: Measuring Quality of Care for Asthma..................................................................49

Quality Measurement.....................................................................................................................49

Multiple Dimensions of Quality for Asthma Care ........................................................................52

Data Sources for Asthma Quality of Care ....................................................................................55

Using Benchmarks To Develop State Performance Estimates ......................................................59

Factors That Affect Quality of Asthma Care ................................................................................66

Module 5: Moving Ahead – Implications for State Action .....................................................74

References ................................................................................................................................77

Appendixes:

A. Acronyms Used in This Resource Guide..........................................................................83

B. Medicaid Spending on Asthma by State ..........................................................................85

C. National and State Asthma Programs ...............................................................................92

D. Asthma Measures............................................................................................................104

E. BRFSS Measures, Data, and Benchmarks......................................................................113

F. Other Asthma-Related Data Sources ..............................................................................132

G. Benchmarks From the NHQR.........................................................................................139

H. Information on Statistical Significance...........................................................................141

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List of Text Tables and Figures Figures

1.1 Children and all ages: Twelve month asthma prevalence 1980-1996, lifetime diagnosis and 12-month attack prevalence 1997-2003, and current prevalence 2001-2003 ........................8

1.2 Asthma hospitalizations per 100,000 population, 2001 ......................................................14 2.1 State-led quality improvement—Stage 1 ............................................................................32 2.2 State-led quality improvement—Stages 1 and 2 .................................................................33 2.3 Complete State-led quality improvement framework—Stages 1, 2, and 3 .........................34 4.1 Six quality measures for asthma: National average, best-in-class average, and State variation, by region, 2003 .....................................................................................................61 4.2 Percent of adults with asthma with routine checkups, medications, urgent care visits, and

emergency room visits, 2003: Maryland compared to benchmarks ....................................62 4.3 Percent of adults with asthma with routine checkups, medications, urgent care visits, and

emergency room visits, 2003: Michigan compared to benchmarks ....................................63 4.4 Percent of adults with asthma with routine checkups, medications, urgent care visits, and

emergency room visits, 2003: New Jersey compared to benchmarks .................................64 4.5 Percent of adults with asthma with routine checkups, medications, urgent care visits, and

emergency room visits, 2003: Vermont compared to benchmarks .....................................65 4.6 Factors that affect disease process and outcome measures .................................................67 Text tables

1.1 Lifetime asthma prevalence for adults (number of cases per 100 population), by State, 2000-2003 ............................................................................................................................10

1.2 Potential for improvement: Percent of asthma hospitalizations that would need to be reduced to achieve best-in-class performance, by State and age group, 2001 ...................11 1.3 Estimate of indirect, direct and total cost burden of asthma, by State, for 50 States, District

of Columbia, and Puerto Rico, 2003 .....................................................................................20 1.4 Medicaid eligible population and estimated asthma prevalence and expenditures for

medical care for age groups 0-18, 19-64, and 65 and over, by State, 2003 ..........................21 4.1 Dimensions of asthma care measurement..............................................................................53 4.2 Six quality measures for asthma: National average, best-in-class average, and poorest

performing average, 2003......................................................................................................56 4.3 Asthma hospitalizations by race/ethnicity and community income, United States, 2001 .....68

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Introduction Improving the quality of health care in America remains a widely shared national objective. The ultimate goal of quality improvement is to close the gap between current practice and best practice as defined by the medical community. Closing this gap can contribute to improved health care in a number of ways: reduced costs, more efficient care delivery, fewer complications, and better quality of life for patients. The National Healthcare Quality Report (NHQR) and National Healthcare Disparities Report (NHDR) published annually by the Agency for Healthcare Research and Quality (AHRQ) provide extensive research and data on the extent of health care quality gaps as well as national benchmarks for quality. This Resource Guide draws on the NHQR and the NHDR to support State-level efforts to improve the quality of care for asthma. It is an update to the second Resource Guide and Workbook published by AHRQ; the first Resource Guide and Workbook addressed diabetes quality of care. This Resource Guide combines the data assembled for the NHQR and other sources with a variety of background, analysis, and policy information on asthma.

Why Should States Make Asthma a Priority? Asthma is a chronic condition that affects the lungs and is characterized by episodes of wheezing, breathlessness, chest tightness, and coughing. During an asthma attack, the airways that carry oxygen to the lungs become inflamed and swollen; the muscles surrounding the airways tighten; and mucus collects, making it harder to push air in and out of the lungs. These episodes are usually the result of exposure to asthma “triggers.” These include infections such as colds and bronchitis; irritants such as second-hand tobacco smoke, dust mites, air pollution, and cockroach debris; other allergens such as furry pets and mold; and other triggers such as stress, exercise, and abrupt changes in the weather. The prevalence of asthma among Americans has nearly doubled in the past two decades. In 2007, 29.3 million people had been diagnosed with asthma at some point in their lives and nearly 23 million people stated they currently had asthma (Centers for Disease Control and Prevention [CDC], 2009; Pleis and Lucas, 2009). Asthma is also a costly disease: the estimated cost of asthma was $19.7 billion in 2007. This total is composed of direct costs—estimated at $14.7 billion from physician visits, hospital stays, and medications—and indirect costs—estimated at $5 billion from lost work days, school absenteeism, and lost earnings (American Lung Association [ALA], 2009). For several years, asthma has been a target for quality improvement efforts by States and other health care entities because of the following:

• Increased prevalence of asthma, especially among children and adolescents.

• Disparities between socioeconomic groups and between racial/ethnic groups in terms of diagnoses and quality of asthma care.

• A range of interventions and treatments that can successfully manage the disease and prevent attacks.

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• High health care cost of uncontrolled asthma and the potential for a positive return on investment for purchasers and the health care system as a whole through asthma quality improvement.

Data from the NHQR and NHDR demonstrate that there are wide variations in quality of care for asthma across States and across different socioeconomic strata and racial and ethnic groups.

Why and How To Use This Resource Guide State leaders can play a central role in leading asthma care quality improvement. This Resource Guide is designed to equip them with information resources and a model for taking action. Purpose of the Resource Guide The purpose of this Resource Guide and companion Workbook is to assist State policymakers and others in planning and implementing a State-level quality improvement initiative for improving asthma care. Specifically, the Resource Guide: • Describes the need for improvement in quality of care for asthma and the potential for returns

on State investments. • Offers a model for how State leaders can lead efforts to improve asthma care quality, along

with examples of State-level activities underway. • Presents examples of current State-led efforts to improve asthma care. • Presents the multiple dimensions within which health care quality for asthma can be

measured, examines metrics for assessing State performance, and provides data from the NHQR and other data sources on asthma to help inform State decisionmaking.

Audiences for This Resource Guide Quality health care is delivered by providers in clinical settings. Thus, quality improvement ultimately needs to influence what happens in a doctor’s office, hospital, or clinic. Even so, State leaders and policymakers can have an enormous impact on health care: • They can articulate a vision that inspires action and change. • They can involve strategic partners and champions who can reach the front lines of health

care. • They can assemble information that focuses the attention of health care providers at the local

level, just as the NHQR does at the national and State levels. • They can enable health care improvement strategies to be tailored more skillfully for State

and local health care markets. As purchasers and regulators, States can supply incentives for providers to make the changes necessary to improve the quality of health care. Thus, the main audiences for this Resource Guide include:

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• State elected leaders — Governors and legislators (and their staffs) who provide leadership on health policy.

• State executive branch officials — Executive office appointees and career staff charged with taking action on important health issues, such as State health department and State Medicaid officials.

• Nongovernmental State and local health care leaders — Members of professional societies, provider associations, quality improvement organizations, voluntary health organizations, health plans, hospital associations, business coalitions, community organizations, consumer groups, and others who want to stimulate action on health care quality improvement at the State level.

Organization of This Resource Guide This Resource Guide is divided into five modules. To assist readers in finding the information they need, the beginning of each module previews the contents and highlights key ideas. Each module ends with a summary and synthesis to demonstrate how to use the module and how to move to the next step. Also, a resource list for further reading and a discussion of associated appendixes are included where applicable. State leaders in different parts of State government have different roles in quality improvement. This Resource Guide is addressed to State leaders, who have key contributions to make to the quality improvement process. Users can skip to the sections that are most relevant and appropriate for them.

The modules are organized as follows: • Module 1: Making the Case for Asthma Care Quality Improvement describes both the

need and opportunity for quality improvement in asthma care. The module answers the following questions: What is asthma? What are current trends in the prevalence of asthma and the cost burden for people with asthma? What opportunities exist for improving care and outcomes for people with asthma and reducing the cost of asthma care?

• Module 2: A Framework for State-Led Quality Improvement presents an operational

approach for leaders to use in their quality improvement efforts. Synthesized from existing models of health care quality improvement, the framework outlines a leadership role for States in setting goals for improvement, convening partners, designing interventions, and assessing their impact through careful measurement and data analysis.

• Module 3: Learning From Current State Quality Improvement Efforts examines current

State efforts to improve the quality of care for asthma. This module summarizes various approaches to asthma quality improvement as they relate to championing quality, creating partnerships, planning for change, implementing the vision, evaluating effectiveness, and spreading success. It also highlights State activities underway at each stage of quality improvement.

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• Module 4: Measuring Quality of Care for Asthma examines measures and data issues that affect asthma care quality and improvement. This module describes current measurement issues and current metrics for assessing asthma care quality and examines a variety of data sources that State leaders can use to assess the quality of care in their States. It provides specific benchmarks of process and outcome measures from the NHQR and the Behavioral Risk Factor Surveillance System (BRFSS) on asthma care. An analysis using BRFSS data from four States—Maryland, Michigan, New Jersey, and Vermont—presents concrete examples of how one can draw conclusions from the data that can spur local action. Finally, the module shows how to derive estimates from available data to fill data gaps for particular States. These include examples for estimating the direct and indirect costs of asthma, Medicaid spending for each State, and cost effectiveness of an asthma intervention for Medicaid primary care case management programs.

• Module 5: Moving Ahead—Implications for State Action describes how State leaders can

initiate a public policy-focused quality improvement effort for asthma care. This module describes specific steps that States can take in each of the three basic areas of activity: lead, partner, and improve.

Supplementary information on data sources and other resources for State leaders as they address asthma care quality improvement are provided in the appendixes. A complementary Workbook mirrors the five modules presented in this Resource Guide and provides a set of exercises and more detailed instructions on how State leaders can find and develop their own State data for asthma care quality improvement. Overall, this Resource Guide and its companion Workbook are designed to be a complete manual for State leaders at all levels interested in improving the quality of care for asthma in their States.

Module 1: Making the Case for Asthma Care Quality Improvement Asthma is a serious chronic respiratory illness that affects a growing number of Americans. According to the National Health Interview Survey (NHIS), conducted by the National Center for Health Statistics (NCHS), 16.2 million adults and 6.7 million children had asthma in 2007, a substantial increase over the prior two decades (CDC, 2008). It is also a costly disease that can seriously impair normal functioning, and it erodes the quality of life for those who have it, as well as their caregivers (Ford, 2003).

Key Ideas in Module 1:

• The number of Americans diagnosed with asthma has grown dramatically in recent years, especially among children and adolescents.

• The cost burden of uncontrolled asthma can be substantial.

• Asthma disproportionately affects African Americans, children, and low-income individuals.

• Quality of care received by people with asthma can vary widely across States and population groups.

• Interventions and treatment can successfully control the disease and prevent attacks.

• There is potential for return on investment for purchasers and the health care system as a whole through asthma quality improvement.

The Need for Asthma Care Quality Improvement Many factors suggest that efforts to improve the quality of asthma care are warranted:

• Increased prevalence of asthma, especially among children and adolescents.

• The high health care cost of uncontrolled asthma.

• The disparities among various socioeconomic, racial, and ethnic groups in how carefully they are diagnosed and treated.

• Variation in interventions and treatment that can successfully manage the disease and prevent attacks.

These points are discussed in more detail in the following sections. Increased Prevalence Cases of asthma have increased dramatically in recent decades. The growth of asthma cases in the United States has been labeled an “epidemic” (RAND, 2002). Information gathered by the CDC from 1980 to 1996 shows that the number of Americans with self-reported asthma more than doubled during that time, from almost 7 million to over 14 million (CDC, 2007a).1 1 Changes in survey design over time make it impossible to compare current data with data collected before 1996.

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Especially troubling are the rates of increase among children: over that 16-year period, asthma prevalence among children under age 5 increased 115 percent. For children between 5 and 14, prevalence increased 81 percent (CDC, 2007b). Figure 1.1 shows the rising trend for children 0-17 and the same for all ages, until 1996. The CDC surveillance survey questions changed in 1997 and began to track asthma attacks in the past 12 months. This modification should reflect more closely changes in the quality of care and self-management practices of people with chronic asthma, especially when compared with the number of people who say they currently have asthma, a statistic which has been collected since 2001. Table 1.1 shows the increase in lifetime asthma prevalence by State between 2000 and 2004. Even in that short period, asthma prevalence increased fairly steadily for nearly all States.

What Is Asthma and How Is It Treated? Asthma is a chronic inflammatory disorder of the airways. Such inflammation can cause recurring episodes of wheezing, breathlessness, chest tightness, and cough, particularly at night and in the early morning. During an asthma attack, the airways that carry oxygen to the lungs become inflamed and swollen, the muscles surrounding the airways tighten, and mucus collects, making it harder to push air out of the lungs. Although asthma triggers are not the cause of asthma itself, they may exacerbate an asthma attack. The most common triggers of asthma attacks are respiratory infections, especially colds. Other triggers include various irritants such as second-hand tobacco smoke, dust mites, air pollution, cockroaches, furry pets, mold, stress, exercise, and changes in the weather. Treatment. The goal of asthma treatment is to reduce underlying inflammation and decrease the daily symptom burden by preventing asthma attacks from recurring. High quality asthma care minimizes the need for emergency care or hospitalization. There are several components of high quality asthma care recommended by the Clinical Guidelines of the National Asthma Education and Prevention Program of the National Heart, Lung, and Blood Institute (NHLBI):

• Component 1: Measures of Assessment and Monitoring. Initial assessment and diagnosis of asthma is extremely important to determine appropriate treatment based on the patient’s level of asthma severity.

• Component 2: Control Factors Contributing to Asthma Severity. Controlling asthma triggers and reducing exposure to environmental allergens and irritants help limit asthma severity. Thus, treatment and prevention of co-occurring respiratory and other conditions (such as rhinitis, sinusitis, chronic obstructive pulmonary disease, and gastroesophageal reflux disease) should be considered.

• Component 3: Pharmacologic Therapy. Medications should be prescribed according to the severity of the patient’s asthma, and medication use should be monitored. Two classes of medications are involved: long-term drugs (inhaled corticosteroids [ICS]) to control the inflammatory process of persistent asthma, and quick-relief medications (beta2-agonists) to treat symptoms and attacks. The objective is to maintain control with ICS and to avoid attacks and the need for emergency treatment.

• Component 4: Education for a Partnership in Asthma Care. Patients and their families play an important role in their asthma care. They need to understand how to monitor their symptoms, what to do during an asthma attack, and how to use their medications appropriately. People with asthma must learn to “manage” their condition so as to avoid triggers and anticipate problems.

Source: National Heart, Lung, and Blood Institute, 2007.

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How Is Asthma Diagnosed and Severity Assessed? Diagnosing asthma and assessing asthma severity are important first steps to quality asthma care. Clinical guidelines for the diagnosis and treatment of asthma were comprehensively updated in 2007. Diagnosing asthma can be difficult and, as a result, it may at times be mislabeled as other problems. Below are steps recommended by NHLBI Clinical Guidelines to diagnose asthma and classify its severity. Methods for diagnosing asthma. The first step in providing quality asthma care is to make a correct diagnosis. Clinical judgment is required because signs and symptoms vary widely from patient to patient as well as within each patient over time. To establish the diagnosis of asthma, the clinician must determine that: 1) episodic symptoms of airflow obstruction or hyperresponsiveness are present; 2) airflow obstruction is at least partially reversible; and 3) alternative diagnoses are excluded. No one test or set of tests is appropriate for every patient. Usually, a detailed medical history, a physical exam focusing on the upper respiratory tract, chest, and skin; and spirometry to demonstrate reversibility of airflow obstruction will enable a clinician to see a pattern of symptoms and history of recurrent episodes and rule out other conditions. Additional tests may be done to evaluate alternative diagnoses, identify triggers, assess severity, and investigate potential complications. Classifying asthma severity. At the initial visit, the physician should assign the patient to a severity grade to help guide medication decisions. The severity classifications are based on the frequency of the patient's symptoms and his or her lung function measurements. The characteristics noted in the chart below are general and may overlap because asthma is highly variable. In addition, the patient's severity classification may change over time. The severity of the patient's asthma should be rechecked at every visit. Severity is currently divided into four levels, as shown in the following table:

Classification of asthma severity

Days with symptoms

Nights with symptoms

FEV1* or PEF* percentage predicted normal

PEF variability between morning and night test

Severe persistent Continual Frequent <60% >30%

Moderate persistent Daily >1 night per

week 60%-80% >30%

Mild persistent >2 days per week but <1 time per day

>2 nights per month >80% 20%-30%

Mild intermittent <2 days per week <2 nights

per month >80 % <20%

*For adults and children over 5 years who can use a spirometer or peak flow meter, the percentage predicted values for forced expiratory volume in 1 second (FEV1) and percentage of personal best for peak expiratory flow (PEF) (NHLBI, 2003). Barriers to diagnosis and severity assessment. Improving asthma care quality requires understanding how asthma is diagnosed and assessed. Asthma care depends on initial assessments and monitoring to determine appropriate care. Patients or their caregivers must be able to give detailed descriptions of frequency and severity of symptoms which are sometimes difficult to recognize. Also, diagnosing asthma in children is difficult because diagnosis may be unclear until recurrence of signs and symptoms is established (NHLBI, 2003). Thus, some patients who actually have asthma may be assessed as having other conditions and may remain untreated until diagnosed accurately. Access to quality lung function testing is often unavailable. These barriers must be addressed to improve asthma care quality.

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Figure 1.1. Children (top) and all ages (bottom): Twelve-month asthma prevalence 1980-1996, lifetime diagnosis and 12-month attack prevalence 1997-2004, and current prevalence 2001-2004

0

20

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140

1980 1985 1990 1995 2000

Pre

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12-month prevalence(“During the past 12 months did anyone in the family have asthma?”)

Lifetime diagnosis(“Has a doctor or other health professional ever told you that you have asthma?”)

Current asthma(Above plus: “Do you still have asthma?”)

12-month attack prevalence(First question above, plus: “During last 12 months have you had an episode of asthma or asthma attack?”)

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1982 1986 1990 1994 1998 2002

Pre

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12-month prevalence(“During the past 12 months did anyone in the family have asthma?”)

Lifetime diagnosis(“Has a doctor or other health professional ever told you that you have asthma?”)

Current asthma(Above plus: “Do you still have asthma?”)

12-month attack prevalence(First question above, plus: “During last 12 months have you had an episode of asthma or asthma attack?”)

Source: Centers for Disease Control and Prevention, National Center for Health Statistics, National Health Interview Survey. Note: Twelve-month asthma prevalence for all ages was collected from 1982 to 1996, a shorter period than for children only.

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What is causing this upsurge in asthma cases? Because doctors are still unsure why some people develop asthma while others do not, further research is needed to identify the exact causes of asthma. Such research is underway at the Environmental Protection Agency, National Institutes of Health, Centers for Disease Control and Prevention, and elsewhere. Risk factors—including genetic predisposition and early exposure to irritants—may contribute, but are certainly not the only reasons for the increase. Even without pinpointing the cause, however, efforts to improve the quality of care for asthma can help control the severity of the condition.

High Cost

Uncontrolled asthma is costly to treat. In recent economic analyses of asthma commissioned by the American Lung Association, the estimated annual cost of asthma in 2004 was $16.1 billion, and in 2007 was $19.7 billion (ALA, 2005, and ALA, 2009). The analyses evaluated both direct costs including physician visits, hospital stays, and medications, as well as indirect costs such as lost work days, school absenteeism, and lost earnings ($14.7 billion direct and $5 billion indirect, respectively). Included in the estimates were:

• 444,000 hospitalizations. • 1.2 million hospital outpatient department visits. • 1.7 million emergency room visits. • 10.6 million doctor office visits. • 12.8 million lost school days. • 1.01 million lost work days.

The most expensive direct cost was prescription drugs ($6.2 billion) [ALA, 2009]). Although the per-person cost of asthma is not the highest among chronic diseases, asthma and chronic obstructive pulmonary disease together represent the fifth most costly disease for the population at $48.7 billion annually, or nearly 6.4 percent of all health care spending (Soni, 2007). Much of this economic burden falls on people with asthma and their caregivers – one study found that the average family in the United States spends between 5.5 percent and 14.5 percent of its total income on treating an asthmatic child (HHS, 2003). In addition, payers also pick up a significant amount of the cost. A study published in February 2002 found that the cost to employers of treating someone with asthma was twice that of treating someone without asthma—$5,385 vs. $2,121 (HHS, 2003). Another study (Brodsky, 2002) found that families spend 2½ times more on children with asthma than on children without asthma—$618.42 vs. $248.67 (in 1996 dollars, inflated to 2003 dollars). As a payer through State Medicaid and State employee health care programs, States have a financial stake in encouraging providers to provide high quality care to plan participants with asthma. Prevention of even a small number of hospitalizations through better management of the disease could affect expenditures significantly. Children are more likely to be hospitalized for asthma than adults (202 per 100,000 children vs. 102 per 100,000 adults ages 18-64 (see Table 1.2). According to a 2006 study, there were 335,000 asthma-related pediatric hospital stays, accounting for 13.5 percent of all pediatric hospitalizations (Stranges, 2008).

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Table 1.1. Lifetime asthma prevalence for adults (number of cases per 100 population), by State, 2000-2004

State 2000 2001 2002 2003 2004Nationwide 10.6 11.2 11.8 11.7 13.3

Alabama 9.1 9.7 11.0 11.6 14.0Alaska 11.3 11.5 11.6 13.3 13.0Arizona 11.1 12.4 13.9 12.5 12.4Arkansas 9.9 10.6 12.1 11.3 12.0California 11.5 12.4 12.7 13.4 14.0Colorado 9.5 12.1 12.1 12.4 13.5Connecticut 10.8 12.3 13.2 12.2 15.3Delaware 10.4 12.0 11.8 11.7 14.4District of Columbia 11.0 12.0 14.2 12.7 15.3Florida 9.1 9.9 10.5 10.1 12.3Georgia 9.6 11.0 11.7 11.8 12.3Guam --- 7.5 12.0 10.3 ---Hawaii 11.4 12.2 13.4 11.6 ---Idaho 10.8 11.7 11.8 11.7 13.1Illinois 10.5 11.3 10.7 11.1 13.1Indiana 11.2 11.3 11.3 12.0 13.3Iowa 8.5 9.7 9.0 10.3 10.5Kansas 10.9 11.7 11.2 11.5 12.0Kentucky 10.7 10.9 12.8 12.6 13.9Louisiana 8.0 9.1 10.4 10.2 11.8Maine 12.5 12.6 13.6 13.4 14.7Maryland 10.6 11.1 12.7 12.3 13.9Massachusetts 11.9 13.1 12.9 14.4 14.9Michigan 10.3 12.4 12.8 13.6 13.4Minnesota 9.5 10.1 11.3 10.5 11.6Mississippi 9.8 9.2 10.6 10.9 11.8Missouri 10.6 12.0 12.5 11.9 13.4Montana 11.4 11.8 14.5 11.1 13.0Nebraska 8.7 8.4 10.6 10.3 10.6Nevada 13.4 13.3 12.4 11.4 12.6New Hampshire 12.0 12.5 13.9 12.9 15.0New Jersey 8.7 9.4 11.8 10.9 13.6New Mexico 10.0 10.8 11.7 10.5 14.7New York 10.7 11.1 11.5 11.7 14.2North Carolina 10.1 10.1 10.9 11.3 13.2North Dakota 9.2 9.1 10.3 10.1 11.0Ohio 10.9 9.8 10.3 10.8 12.3Oklahoma 9.2 10.1 11.2 11.8 13.4Oregon 12.1 13.0 14.0 14.7 16.3Pennsylvania 9.3 10.7 11.5 11.9 13.1Puerto Rico 15.9 19.6 19.6 20.7 18.8Rhode Island 11.7 12.1 12.8 14.4 14.6South Carolina 10.4 10.8 10.0 10.1 11.8South Dakota 8.0 7.7 8.6 10.7 10.3Tennessee 10.4 9.3 12.2 11.8 14.7Texas 10.5 9.6 11.6 11.3 12.9Utah 10.3 10.7 12.3 11.3 13.1Vermont 9.7 12.1 12.7 12.2 15.0Virginia 10.5 11.4 12.1 12.1 13.3Virgin Islands --- 9.2 9.4 9.2 10.3Washington 11.9 12.0 14.3 13.8 15.5West Virginia 11.7 12.5 12.8 11.8 15.5Wisconsin 10.6 10.9 11.7 11.0 12.4Wyoming 11.8 11.6 11.1 11.2 12.7

Source: Centers for Disease Control and Prevention, Behavioral Risk Factor Surveillance System, Prevalence Data, 2000-2004. http://apps.nccd.cdc.gov/brfss/index.asp

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Table 1.2. Potential for improvement: Percent of asthma hospitalizations that would need to be reduced to achieve best-in-class performance, by State and age group, 2004

State Adjusted rate

Percent to be reduced to

achieve best-in-class Adjusted rate

Percent to be reduced to

achieve best-in-class Adjusted rate

Percent to be reduced to

achieve best-in-class

Total U.S. 201.945 -- 102.045 -- 225.930 --Best in class1 72.182 -- 43.005 -- 112.982 --

Arizona 184.584 60.9% 75.364 42.9% 166.514 32.1%Arkansas 144.853 50.2% 101.831 57.8% 209.653 46.1%California 137.269 47.4% 60.367 28.8% 198.930 43.2%Colorado 186.616 61.3% 55.560 22.6% 130.829 13.6%Florida 243.050 70.3% 114.587 62.5% 236.031 52.1%Hawaii 150.322 52.0% 82.814 48.1% 199.991 43.5%Iowa 100.083 27.9% 56.658 24.1% 125.133 9.7%Kansas 168.546 57.2% 80.710 46.7% 164.702 31.4%Kentucky 285.456 74.7% 134.522 68.0% 264.188 57.2%Massachusetts 184.129 60.8% 108.566 60.4% 241.589 53.2%Michigan 224.477 67.8% 123.915 65.3% 224.852 49.8%Minnesota 142.629 49.4% 58.573 26.6% 130.531 13.4%Missouri 214.656 66.4% 101.238 57.5% 177.356 36.3%Nebraska 105.393 31.5% 54.217 20.7% 135.028 16.3%Nevada 142.645 49.4% 85.086 49.5% 148.429 23.9%New Hampshire 65.247 -10.6% 70.778 39.2% 127.655 11.5%New Jersey 244.706 70.5% 137.834 68.8% 277.496 59.3%New York 345.183 79.1% 156.390 72.5% 302.108 62.6%North Carolina 158.698 54.5% 102.792 58.2% 213.224 47.0%Oregon 80.131 9.9% 45.829 6.2% 113.305 0.3%Rhode Island 209.587 65.6% 94.720 54.6% 185.939 39.2%South Carolina 229.531 68.6% 111.987 61.6% 215.607 47.6%Tennessee 194.999 63.0% 107.116 59.9% 238.465 52.6%Utah 100.930 28.5% 36.358 -18.3% 129.507 12.8%Vermont 71.167 -1.4% 46.828 8.2% 111.439 -1.4%Washington 121.465 40.6% 51.636 16.7% 114.201 1.1%West Virginia 228.785 68.4% 143.966 70.1% 347.759 67.5%Wisconsin 135.246 46.6% 74.911 42.6% 149.532 24.4%

1Best in class rate is calculated from the weighted average of the lowest 10 percent of States' hospitalization rates.

Source: Agency for Healthcare Research and Quality, Healthcare Cost and Utilization Project (HCUP), 2004.

Hospital admissions for pediatric asthma per 100,000 population

under age 18

Hospital admissions for adult asthma per 100,000 population

ages 18-64

Hospital admissions for adult asthma per 100,000 population

ages 65+

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Racial, Ethnic, and Income Disparities Asthma does not affect all groups equally. Asthma is more prevalent among minorities and low income persons, and asthma attack rates and mortality are higher among Blacks compared with Whites (AHRQ, 2003). In addition, Black children in the United States are more than 3½ times as likely to be admitted to a hospital for asthma as White children (AHRQ, 2008), Table 117). Black adults age 18 to 64 are more than three times as likely to be hospitalized as White adults for asthma (AHRQ, 2008, Table 118A). The 2005 National Health Interview Survey (CDC, 2006a) showed that:

• Current asthma prevalence is 125 percent higher for Puerto Ricans compared with non-Hispanic Whites. Non-Hispanic Blacks and American Indians and Alaska Natives had 25 percent higher current asthma prevalence compared with non-Hispanic Whites.

• In 2005, Puerto Ricans also had the highest rate of asthma attacks in the previous year, 140 percent higher than non-Hispanic Whites. American Indians and Alaska Natives had an asthma attack rate that was 43 percent higher than non-Hispanic Whites.

• Blacks had an asthma hospitalization rate 240 percent higher than Whites. In 2003, Puerto Ricans were the most likely to die from asthma and had an asthma death rate more than 360 percent higher than Whites. Blacks had an asthma death rate 200 percent higher than Whites. There are also significant racial/ethnic disparities among children in asthma status and self-management practices. A study by Lieu et al. (2002) showed that Black and Hispanic children have more severe asthma based on number of symptom days, missed school days, and health status scores than White children with similar insurance and socioeconomic status. Black and Hispanic children were also less likely than White children to be using daily inhaled anti-inflammatory medications (28 percent and 22 percent, respectively, compared with 33 percent). Income also plays a role. Children in poor families are more likely than other children to have been diagnosed with asthma (14.9 vs. 12.6 percent). And, although not all single-parent families are low income, children in single-mother families are more likely to have asthma (17.1 percent) than children from two-parent families (11.4 percent) or than children from single-father families (11.5 percent) (CDC, 2007b). Another study looking at indoor and outdoor allergies among children with asthma found that Puerto Rican and Black children were at greater risk for multiple allergies. The study found that Puerto Rican children with asthma are up to three times more likely to be allergic to indoor and outdoor allergens than White children with asthma. The study also found that Black children with asthma are two to three times more likely to have allergic reactions to outdoor allergens (Celedón et al., 2004).

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Intervention and Treatment Variation Clinical guidelines for care—including developing an asthma management plan with physicians, eliminating or decreasing exposure to triggers, and proper use of medications—offer people with asthma a way of minimizing its effects on daily living, avoiding hospitalizations, and reducing trips to the emergency room. Data gathered in national surveys, however, show that many people do not have control of their asthma:

• The 2004 NHQR reported that, according to national estimates from the National Committee for Quality Assurance Health Plan Employer Data and Information Set (HEDIS®), nearly a third of children and adults suffering from persistent asthma are not receiving inhaled corticosteroids to control their asthma (AHRQ, 2004b).

• Although most asthma deaths are preventable if care is received in time, 4,055 deaths were attributed to asthma in 2003 (CDC, 2006a).

• The Medical Expenditure Panel Survey determined that only one-third of respondents with asthma in 2002 used a peak flow meter recommended at that time to self-monitor the severity of their asthma (MEPS, 2002).

There is also considerable variation from State to State in the care received by people with asthma. The following chart and table show two of the asthma measures that are available nationwide—hospitalizations for asthma and use of inhaled corticosteroids—with data for States grouped by region to allow for regional comparisons.2 Comparisons can also be made across all States to the national average and the best-in-class average (the 10 percent of States with the best value). The percentage of people receiving specific, recommended services and the percentage difference between the lowest and the highest performing State vary by service.3 The use of the most expensive service—inpatient care—varies three to five times across the States and shows variation within each region, especially for children (Figure 1.2 and Table 1.2). For every 100,000 State adult residents age 18 to 64, from 36 to 156 people will be admitted to the hospital with asthma. For every 100,000 State child residents, from 65 to 345 children will be admitted (HCUP, 2004). Little of the variation in hospitalizations is likely to be due to differences in asthma prevalence across States (see Table 1.1). Asthma prevalence rates only ranged from 10.5 to 16.3 percent across the States represented in the HCUP data. Thus, the top State in terms of prevalence has 55 percent more residents with asthma than the bottom State. Contrast that with the top State in terms of pediatric hospitalizations, which has 429 percent more children admitted to the hospital during a year than the State with the lowest hospitalization rate for children.

2 U.S. Census regions are: Northeast=CT, ME, MA, NH, RI, VT, NJ, NY, PA); Midwest=IN, IL, MI, OH, WI, IA, KS, MN, MO, NE, ND, SD; South=DE, DC, FL, GA, MD, NC, SC, VA, WV, AL, KY, MS, TN, AR, LA, OK, TX; West=AZ, CO, ID, NM, MT, UT, NV, WY, AK, CA, HI, OR, WA. 3 For example, BRFSS data for 2004 show that receipt of flu shots among adults with asthma varied by State from 22 percent to 62 percent, a difference of 40 percentage points, while the proportion of adults who had an emergency room visit for asthma ranged from 9 percent to 41 percent, a difference of 32 percentage points. Regional and State variation is discussed further in Module 4: Measuring Quality of Care for Asthma.

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Figure 1.2. Asthma hospitalizations per 100,000 population, 2004

Source: Agency for Healthcare Research and Quality, Healthcare Cost and Utilization Project, 2004. Use of inhaled corticosteroids by people with persistent asthma—measured from health plan claims across regions—varied from 2001 to 2003 as shown below:

Use of inhaled corticosteroid medications by people with asthma, by U.S. Census region

2003 2001

Region No. of plans

Mean %

Standard error

No. of plans

Mean %

Standard error

National average 408 69.7 16.6 417 65.0 14.6 Best-in-class average 190 72.1 Northeast 89 73.1 17.6 91 66.9 23.8 South 101 71.2 24.9 128 63.9 20.1 Midwest 109 70.6 32.2 119 68.4 17.9 West 103 66.3 33.4 85 63.1 40.3 None reported 6 43.0 208.9 4 36.8 210.5

Note: All means are weighted by the eligible populations of the plans. Source: National Committee for Quality Assurance, HEDIS data from The State of Healthcare Quality, 2004.

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This variation suggests possible bias in terms of which plans report fully or which are regional versus national plans. Because of the large difference between reporting and non-reporting plans, full reporting might change the above regional estimates significantly. These regional 2003 HEDIS averages compare with the national average of 69.6 percent from the 2004 BRFSS and the best-in-class State average of 76.5 percent for use of this important type of medication. Regardless of data sources (State-run surveys, claims for payment, or hospital discharges) and regardless of differences in asthma prevalence, there is considerable variation in asthma care. These figures illustrate this variation across States and regions for asthma measures. This variation suggests room for improvement for many States. The States with the best rates on the asthma measures—the best-in-class States—provide examples of quality performance that is achievable. However, even the best results may leave room for improvement. Implications for State Policy Disparities in the prevalence and management of asthma and in quality of care have important implications for States and the public sector more generally. Care for low income individuals who are hospitalized is often financed by public sources such as Medicaid and uncompensated care funds. Ensuring effective care can help people with asthma remain healthy and productive, prevent attacks, and reduce health care costs. These differences are important for two reasons as States undertake asthma quality improvement initiatives. First, the racial, ethnic, and socioeconomic makeup of a given State influences the prevalence of asthma in the State. Second, improvement in quality of care may require targeted efforts to minority and low income groups in order to be successful.

The Quality Improvement Opportunity

Despite this gloomy picture of asthma’s care quality and cost burdens, significant opportunities for improvement exist. There is potential for high returns on investment made by purchasers and the health care system as a whole through asthma care quality improvement. Availability of Asthma Management Guidelines Great strides in the care and treatment of people with asthma have occurred over the last 15 years. Although there is no cure for asthma, the disease can be managed and the severity and frequency of asthma attacks can be controlled through appropriate monitoring, effective use of medications, and eliminating or decreasing exposure to triggers. In 1997, Guidelines for the Diagnosis and Management of Asthma was published by the National Asthma Education and Prevention Program (NAEPP), coordinated by NHLBI. These Guidelines (updated in 2007) represent a science-based strategy for the diagnosis and management of asthma and ask patients, families, and providers to work together to control the condition. In addition the NAEPP has published Key Clinical Activities for Quality Asthma Care: Recommendations of the National Asthma Education and Prevention Program, which identifies four components of care and recommends a core set of 10 key clinical activities for ensuring quality asthma care, as follows:

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Components of asthma care Key associated clinical activities

1. Establish asthma diagnosis.

2. Classify severity of asthma.

3. Schedule routine follow-up care. Assessment and monitoring

4. Assess for referral to specialty care.

5. Recommend measures to control asthma triggers. Control of factors contributing to asthma severity 6. Treat or prevent comorbid conditions.

7. Prescribe medications according to severity. Pharmacotherapy

8. Monitor use of beta-2-agonist drugs.

9. Develop a written asthma management plan. Education for partnership in care

10. Provide routine education on patient self-management.

Source: National Heart, Lung and Blood Institute, 2003. More information on steps associated with these key clinical activities and updates to the Guidelines is available at: http://www.nhlbi.nih.gov/health/prof/lung/asthma/asthmacare.pdf. By applying these guidelines, health care professionals can provide the best care available for their patients. In the future, guidelines could change. And, to provide the best treatment possible for their patients, clinicians must keep abreast of changes in the best practices. Much remains to be done in improving the scientific basis for clinical practice across all of medical care, and asthma is no exception. An AHRQ-supported Evidence-based Practice Center conducted a systematic review of interventions for the management of asthma in 2001. The report (BCBS Technology Evaluation Center, 2001) examined five types of asthma interventions and concluded the following:

• Chronic use of inhaled corticosteroids (ICS) for children with mild-to-moderate asthma improves their long-term outcomes; however, studies had insufficient follow-up time or patient numbers to assess the cumulative effects of using ICS.

• Evidence is insufficient for showing that early initiation of ICS prevents asthma progression.

• Limited evidence suggests that ICS dosage may be reduced without diminishing asthma control.

• Limited evidence also suggests that there is no benefit to using antibiotics routinely in addition to ICS.

• There is insufficient evidence to determine whether the use of a written asthma action plan, including a peak-flow meter-based vs. a symptom-based plan, improves outcomes.

These inconclusive findings illustrate the early stage of research on asthma care quality. Nevertheless, the expert judgment of clinical specialists, assembled by the NAEPP, establishes the best practice today for helping patients and providers achieve optimal asthma care.

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Potential for Positive Return on Investment State government officials want programs that improve the health of their residents; but at the same time, they must weigh the cost of those programs against all of the competing demands of society. Therefore, for State officials to wear the mantel of quality improvement, such programs must result in enough savings to offset their expense, at the very least. Research suggests that investing in asthma prevention and control initiatives can improve health outcomes and reduce health care costs. Just as clinical research on effective asthma care is new and emerging, so is research on the return on investment for asthma quality improvement. A systematic review of return on investment for asthma suggests positive potential financial savings (Goetzel et al., 2005).4 In that review, $2.72 was saved for every dollar spent on asthma disease management programs, on average, across six studies that provided sufficient data to calculate per-participant cost savings relative to program costs. The average program cost was $269 and the average cost saving was $729 per participant. Thus, while it is early to draw definitive conclusions, the results are quite promising. One of the reviewed studies evaluated an asthma intervention, the Virginia Health Outcomes Partnership (VHOP), targeted to reduce emergency visits by low-income asthma patients in a Medicaid primary care case management program (Rossiter et al., 2000). About 20 percent of Medicaid asthma-related claims in Virginia were for emergency department visits (Rossiter, 2005). The VHOP invited physicians in one community to participate in training to improve their management of patients with asthma, including patient education, medication use, and need for emergency care. The VHOP also provided feedback reports to participating physicians on their patients’ use of services. One-third of about 200 physicians invited actually participated. These physicians reduced their patients’ use of emergency services by 41 percent from the same quarter a year earlier, compared to only an 18-percent reduction for a comparison group that was not invited to participate. All of the 200 physicians invited to participate (counting those not trained) reduced their patients’ use of emergency services by 6 percent more than the non-intervention group. At the same time, physicians in the participating community dispensed more asthma medications. The increased drug costs were more than offset by lower emergency care costs. The projected direct savings to Medicaid was $3 to $4 for every dollar spent on training for participating physicians. More recent studies also support the conclusion that disease management programs for asthma can save money. Patients of physicians who participated in another asthma education program were less likely to be admitted to an emergency room or a hospital to treat their asthma than patients whose physicians did not participate (Brown et al., 2004). An asthma disease management program implemented by Colorado Medicaid from 2002 to 2003 showed that the program saved $203,000 in health care expenditures beyond the cost of the program, compared to the pre-program costs of treating asthma (National Jewish Medical and Research Center, 2004). Not only did emergency room visits decline, but missed work days also declined.

4 This review found 12 studies. However, only 6 provided sufficient data for a return on investment calculation, and some of those studies were limited by small numbers of cases, incomplete patient care costs, and study designs that did not control for rising health care costs and other shifting external factors.

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These interventions can deliver substantial cost savings if they reduce the number of repeat hospitalizations and emergency visits. A study using 1997 data found that each hospitalization increased annual expenditures for asthma significantly—from $305 for someone not hospitalized, to $1,690 for someone hospitalized once, to $5,987 for someone hospitalized twice (Atherly et al., 2003).

Thus, not only can health care professionals improve asthma care to help their patients achieve better control of asthma symptoms and improve their lives, they can also reduce the use of expensive health care services and, thereby, cut the cost of asthma care. These consequences of quality improvement would benefit not only consumers of health care, but also the two other groups that bear the cost—third-party payers (public and private) who incur the cost of asthma care and employers who incur the cost of health insurance and lost productivity for their workers with asthma.

Estimating the Costs of Asthma Care and Potential Savings From Quality Improvement

To bring the potential of quality improvement home, State officials will want to know what the potential cost savings are in their State. For example, what could be saved in Medicaid costs? Medicaid recipients are an important focus since they include people with low incomes and children who have higher prevalence and hospitalization rates for asthma (CDC, 2007a; CDC, 2007b). This section estimates the cost of asthma care from three perspectives: (1) the cost of asthma care statewide, (2) the cost for Medicaid, and (3) the cost of excess hospitalizations for asthma. Next, this section guides State analysts through the steps they could take to estimate the potential savings in the State while implementing a Medicaid disease management program in asthma like the one in Virginia. (Those savings were not calculated here because the number of physicians participating in Medicaid in each State was not available.) A caveat about estimating costs. Data on the cost of asthma care are not available uniformly across States. Some States may have tallied the costs for their Medicaid recipients, but probably few States have estimated the costs of asthma for their entire population. The numbers in this section simply apply various national averages from published research to State data to estimate what the cost might be in each State. Where possible the national averages are age or race specific. To assume that the cost for every State by age and racial subgroup will equal the national subgroup is unrealistic.5 Therefore, AHRQ urges State analysts to use local data to develop better estimates of the cost of asthma for their State. The numbers presented are intended to help State and local officials think about the scale of problem and of the impact that they might be able to make with quality improvement initiatives for asthma. 5 Several other factors are not accounted for in these estimates: First, changes in the typical services used between 1994 and 2004 are excluded, despite that fact that medication costs have risen (Sullivan et al., 1996), and inpatient stays have declined (Mannino et al., 1998). Second, differences in use of services by age are not always included, despite the fact that from 1985 to 1994 the estimated real direct cost of asthma care actually declined per affected child, but increased per adult (Weiss et al., 2000). Third, differences in the age distribution across racial/ethnicity groups is not factored into the State-level estimates. Finally, the asthma cost calculated here is not net of health care cost without chronic disease because it was not available; subtracting the cost of those without chronic illness from those with asthma would indicate how much a State spends for asthma care alone. Thus, the State-level estimates in this section could overestimate or under estimate of today’s true cost of asthma to States and their residents.

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Cost of Asthma Care Statewide A statewide view of asthma costs is provided to encourage States to stimulate quality improvement on a statewide basis, not only in Medicaid. Three sources were combined to calculate the direct cost of medical care on a statewide basis: Weiss et al. (2000) for national expenditure data, the U.S. Census for State population estimates, and the Behavioral Risk Factor Surveillance System for State-level asthma prevalence. Direct costs include medical expenditures for hospital care, physician services, and medications. The Weiss study, which provides expense per person with asthma, is for the year 1994 and was updated to 2004 here, using the medical care component of the Consumer Price Index. The total cost for asthma care in the State was calculated by multiplying the per-person cost by the number of people with asthma in the State. Table 1.3 shows the calculated estimates by State for 2004. Across all the States, spending on asthma care (direct costs) totaled to over $15 billion, according to these estimates. The difference in totals between the ALA report and the summed State estimates here points out the imprecision of the method, noted in the caveat above. Thus, State analysts should attempt to develop these estimates with their own data. Expenditures on asthma in the top four States in asthma costs—California, Texas, New York, and Florida—together were estimated at over $8 billion. Improving asthma care and reducing avoidable admissions and emergency care might save health care systems in States substantial dollars.

Cost of Asthma Care for Medicaid Three components were used to estimate the cost of asthma care for Medicaid:

• National asthma prevalence separately by age and by race/ethnicity. • State Medicaid populations separately by age and by race/ethnicity. • Estimated national expenditures per person with asthma.

Data sources for each of these components are listed below.

Components needed to estimate Medicaid costs of asthma

Source of information

National asthma prevalence separately by age and by race/ethnicity

CDC Health Data Interactive Web site available at: http://205.207.175.93/hdi/ReportFolders/ReportFolders.aspx?IF_ActivePath=P,19

State Medicaid populations separately by age and by race/ethnicity

Centers for Medicare & Medicaid Services (CMS) Web site available at: http://www.cms.hhs.gov/MedicaidDataSourcesGenInfo/02_MSISData.asp

Estimated national expenditures per person with asthma

Weiss KB, Sullivan SD, Lyttle CS. Trends in the cost of illness for asthma in the United States, 1985-1994. J Allergy Clin Immunol. September 2000; 106(3):493-99.

Note: See Appendix Figure B.1 for more information on the flow of data, assumptions, and calculations made to derive Medicaid spending for asthma by State and Appendix Tables B.1-B.6 for subgroups eligible for Medicaid in each State by age and race/ethnicity.

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Table 1.3. Estimate of indirect, direct and total cost burden of asthma, by State, for 50 States, District of Columbia, and Puerto Rico, 2004

StatePopulation estimate1

Percent of population with

asthma2Asthma

prevalenceIndirect asthma costs for State3

Direct asthma costs for State3

Total asthma costs for State4

Nationwide 296,786,058 8.1 24,186,627 $11,546,882,780 $15,288,466,328.1 $26,835,349,108

Alabama 4,506,574 8.6 386,069 184,312,317 244,035,789 428,348,106Alaska 660,975 9.0 59,258 28,290,260 37,457,269 65,747,529Arizona 5,750,475 7.2 412,436 196,899,955 260,702,251 457,602,206Arkansas 2,740,191 7.4 201,991 96,432,174 127,679,484 224,111,658California 35,629,666 7.7 2,732,892 1,304,703,649 1,727,472,097 3,032,175,746Colorado 4,600,050 8.7 398,659 190,322,970 251,994,098 442,317,068Connecticut 3,475,351 9.7 335,807 160,317,085 212,265,285 372,582,370Delaware 825,682 10.0 82,249 39,266,502 51,990,187 91,256,690District of Columbia 579,521 9.2 53,110 25,355,163 33,571,100 58,926,263Florida 17,313,811 7.3 1,259,028 601,069,835 795,836,947 1,396,906,782Georgia 8,910,741 7.4 656,849 313,584,755 415,196,903 728,781,658Hawaii 1,251,532 8.3 103,476 49,400,285 65,407,661 114,807,945Idaho 1,390,329 8.0 110,797 52,895,286 70,035,161 122,930,447Illinois 12,665,718 8.4 1,059,813 505,962,700 669,911,858 1,175,874,557Indiana 6,210,801 8.4 519,693 248,105,448 328,500,069 576,605,517Iowa 2,942,739 6.6 193,471 92,364,500 122,293,745 214,658,245Kansas 2,731,069 7.4 201,319 96,111,154 127,254,444 223,365,598Kentucky 4,135,567 8.3 341,927 163,238,485 216,133,317 379,371,801Louisiana 4,487,830 6.2 277,171 132,323,655 175,201,029 307,524,683Maine 1,307,904 9.6 125,074 59,711,297 79,059,792 138,771,088Maryland 5,538,989 7.8 430,373 205,463,417 272,040,566 477,503,983Massachusetts 6,437,414 9.7 622,018 296,956,322 393,180,291 690,136,612Michigan 10,090,280 8.3 834,260 398,282,029 527,338,980 925,621,009Minnesota 5,078,014 7.5 379,381 181,119,228 239,808,030 420,927,258Mississippi 2,884,596 7.1 204,016 97,398,607 128,959,075 226,357,683Missouri 5,742,650 9.1 520,563 248,521,028 329,050,311 577,571,338Montana 925,969 8.6 79,326 37,870,784 50,142,209 88,012,993Nebraska 1,741,450 6.9 119,696 57,143,851 75,660,406 132,804,256Nevada 2,323,875 7.1 164,358 78,465,819 103,891,419 182,357,238New Hampshire 1,292,064 10.3 132,569 63,289,352 83,797,259 147,086,612New Jersey 8,620,770 8.6 738,524 352,576,945 466,823,891 819,400,836New Mexico 1,889,266 9.3 175,023 83,557,504 110,632,983 194,190,487New York 19,301,113 8.9 1,711,167 816,924,199 1,081,635,480 1,898,559,679North Carolina 8,523,199 7.5 636,772 303,999,796 402,506,091 706,505,887North Dakota 636,196 7.7 48,798 23,296,520 30,845,387 54,141,908Ohio 11,445,095 8.5 969,077 462,644,846 612,557,543 1,075,202,389Oklahoma 3,511,960 8.3 290,367 138,623,562 183,542,320 322,165,882Oregon 3,576,262 9.7 345,558 164,972,085 218,428,663 383,400,748Pennsylvania 12,335,652 8.8 1,081,346 516,243,007 683,523,335 1,199,766,343Puerto Rico 3,893,931 6.2 240,492 114,812,544 152,015,722 266,828,266Rhode Island 1,071,095 9.6 102,428 48,899,974 64,745,232 113,645,205South Carolina 4,196,799 7.6 317,725 151,684,485 200,835,428 352,519,914South Dakota 773,539 6.7 51,627 24,647,134 32,633,645 57,280,778Tennessee 5,906,936 9.0 529,572 252,821,600 334,744,415 587,566,015Texas 22,424,884 7.1 1,586,019 757,177,944 1,002,529,403 1,759,707,346Utah 2,439,852 8.0 194,435 92,824,554 122,902,873 215,727,427Vermont 618,432 8.5 52,364 24,998,864 33,099,348 58,098,213Virginia 7,454,688 7.3 542,091 258,798,487 342,658,017 601,456,503Washington 6,179,645 9.2 566,332 270,371,403 357,980,952 628,352,356West Virginia 1,803,312 10.1 181,431 86,616,703 114,683,466 201,300,169Wisconsin 5,508,789 8.6 471,927 225,301,452 298,306,801 523,608,252Wyoming 502,816 7.7 38,567 18,412,350 24,378,579 42,790,928

4The sum of the state estimates dif fer from the national estimate in the first row by about 1 percent due to rounding.

1U. S. Census annual estimates of the population for the United States, and for Puerto Rico: April 1, 2000 to July 1, 2004, accessed at: http://www.census.gov/popest/states/NST-ann-est2004.html2 Prevalence based on BRFSS 2004 estimates for adults augmented for different ial between all ages and adults nationwide. For BRFSS see: http://apps.nccd.cdc.gov/brfss/list.asp?cat=AS&yr=2004&qkey=4416&state=All and for CDC national prevalence by age see: http://205.207.175.93/HDI/TableViewer/tableView.aspx?ReportId=603Calculations of direct cost per person, are based on Weiss et al. (2000), inflated by medical care component of CPI to 2004. Indirect cost per person are based on Weiss et al. (2000) inflated by percent change in average annual wages through 2004. Weiss KB, Sullivan SD, Lyttle CS. Trends in the cost of illness for asthma in the United States, 1985-1994. J Allergy Clin Immunol 2000 Sep;106(3):493-9.

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Table 1.4 shows the estimated expenditures likely to occur by State Medicaid agency, based on the above calculations. Nationally, Medicaid programs spent, according to these estimates, over $4 billion dollars on asthma alone. The States with the highest expenditures (California, Texas, New York, and Florida) spent over $1.8 billion for asthma care for their Medicaid enrollees. Table 1.4. Medicaid eligible population and estimated asthma prevalence and expenditures for medical care for age groups 0-18, 19-64, and 65 and over, by State, 2004

State

Medicaid population age

0-18 with asthma1,2

Estimated Medicaid expense for age 0-18 with

asthma 3

Medicaid population age

19-64 with asthma1,2

Estimated Medicaid expense for age 19-64 with

asthma3

Medicaid population age

65 and over with asthma1,2

Estimated Medicaid expense

for age 65 and over with asthma3

Total estimated Medicaid spending on asthma

Total US 2,490,162 $2,762,864,614 1,495,216 $1,658,959,559 367,589 $407,843,883 $4,829,668,056

Alabama 38,710 42,949,509 22,755 25,247,402 7,610 8,442,977 76,639,889Alaska 7,034 7,804,689 2,503 2,777,450 471 522,940 11,105,078Arizona 56,803 63,023,309 42,775 47,459,833 5,375 5,963,662 116,446,804Arkansas 33,407 37,065,885 16,300 18,085,041 3,928 4,358,105 59,509,031California 375,857 417,018,055 354,222 393,013,154 55,957 62,084,579 872,115,787Colorado 25,869 28,701,536 11,314 12,552,861 3,181 3,529,001 44,783,397Connecticut 21,677 24,051,405 12,640 14,024,487 3,993 4,430,678 42,506,570Delaware 6,550 7,267,791 5,167 5,733,110 763 846,045 13,846,946District of Co lumbia 6,927 7,686,003 4,324 4,797,328 879 974,894 13,458,225Florida 133,036 147,605,412 62,103 68,904,224 23,182 25,720,149 242,229,785Georgia 90,616 100,539,397 35,189 39,042,880 10,389 11,526,972 151,109,249Hawaii 8,815 9,780,075 6,486 7,196,281 1,411 1,565,654 18,542,011Idaho 12,498 13,867,029 4,020 4,460,545 831 921,782 19,249,357Illino is 102,508 113,733,823 45,906 50,933,795 23,087 25,615,256 190,282,873Indiana 49,771 55,221,121 21,155 23,471,824 4,983 5,528,150 84,221,095Iowa 18,265 20,265,440 9,598 10,649,490 2,564 2,844,256 33,759,186Kansas 17,219 19,104,169 7,221 8,011,925 2,077 2,303,906 29,420,000Kentucky 37,031 41,086,078 20,388 22,620,914 5,771 6,403,500 70,110,492Louisiana 60,176 66,766,180 19,752 21,915,131 6,766 7,507,161 96,188,473Maine 9,884 10,966,548 10,415 11,555,512 2,135 2,368,308 24,890,369Maryland 39,562 43,894,567 20,052 22,247,904 4,958 5,500,957 71,643,428Massachusetts 41,101 45,602,596 35,605 39,504,432 8,736 9,693,024 94,800,053Mich igan 81,334 90,241,556 45,505 50,487,891 8,256 9,160,665 149,890,112Minnesota 31,172 34,585,287 18,715 20,764,275 5,580 6,190,955 61,540,518Mississippi 37,451 41,552,140 16,642 18,464,992 5,935 6,585,379 66,602,512Missouri 54,908 60,921,518 30,678 34,037,601 6,274 6,960,719 101,919,838Montana 5,243 5,817,146 2,739 3,038,845 648 718,797 9,574,787Nebraska 13,703 15,204,154 5,036 5,587,266 1,511 1,676,525 22,467,944Nevada 12,475 13,841,080 5,768 6,400,127 1,479 1,640,605 21,881,813New Hampshire 6,812 7,558,165 2,673 2,965,791 875 971,003 11,494,960New Jersey 44,490 49,362,205 21,550 23,909,743 8,870 9,840,991 83,112,939New Mexico 26,400 29,291,657 11,272 12,506,094 2,027 2,248,874 44,046,625New York 169,245 187,779,155 159,707 177,196,516 31,651 35,116,711 400,092,382North Carolina 69,173 76,748,742 35,618 39,518,809 11,162 12,384,508 128,652,059North Dakota 3,194 3,543,548 1,858 2,060,969 599 664,165 6,268,681Ohio 91,345 101,348,167 50,979 56,561,476 9,881 10,963,639 168,873,282Oklahoma 37,529 41,639,376 11,828 13,123,041 4,053 4,497,129 59,259,545Oregon 23,081 25,608,648 17,960 19,926,693 3,114 3,455,074 48,990,414Pennsylvania 79,979 88,737,658 48,686 54,017,859 13,724 15,227,304 157,982,821Rhode Island 8,751 9,709,721 5,901 6,547,641 1,543 1,711,489 17,968,850South Caro lina 43,054 47,769,146 22,902 25,410,397 8,791 9,753,628 82,933,171South Dakota 6,575 7,294,857 2,300 2,551,513 763 846,458 10,692,828Tennessee 58,656 65,079,755 50,391 55,908,976 10,950 12,149,506 133,138,237Texas 218,531 242,462,453 60,382 66,994,197 25,087 27,834,679 337,291,329Utah 14,296 15,861,343 7,541 8,367,319 892 989,344 25,218,006Vermont 5,924 6,572,266 4,854 5,385,075 1,324 1,468,936 13,426,277Virginia 40,341 44,758,613 16,354 18,145,315 6,336 7,029,631 69,933,559Washington 55,832 61,946,784 30,409 33,738,915 5,226 5,798,498 101,484,197West Virginia 16,127 17,893,248 10,016 11,113,064 2,103 2,333,210 31,339,522Wisconsin 37,023 41,077,590 25,527 28,322,682 9,552 10,597,629 79,997,901Wyoming 4,198 4,658,017 1,533 1,700,953 339 375,877 6,734,847

Note: Age groups differ slightly depending on source. Population age groups for Medicaid eligibles are 0-18, 18-64, 65+, while NHIS prevalence rates are 0-17years, 18-64 years, and 65+. Also, est imates based on age only by State (shown above) can d if fer substantia lly from estimates based on race only by State (shown in Tables B.1 through B.6). Combined age-race prevalence of asthma by State is not available.

1 Centers for Medicaid and Medicare Services, MSIS Sta te Summary FY 2004, accessed a t http://www.cms.hhs.gov/MedicaidDataSourcesGenInfo/02_MSISData.asp. 2 Ca lculations of prevalence rates based on Centers for Disease Control and Prevent ion, Health Data for All Ages, National Center for Health Statistics, accessed at h ttp://205.207.175.93/HDI/TableViewer/tableView.aspx?ReportId=60.3 Calculat ions of direct cost per person, are based on Weiss e t al. (2000), inflated by medical care component of CPI to 2004. Indirect cost per person are based on W eiss et al. (2000) inflated by percent change in average annual wages through 2004. W eiss KB, Sullivan SD, Lyttle CS. Trends in the cost of illness for asthma in the United States, 1985-1994. J Allergy Clin Immunol 2000 Sep;106(3):493-9.

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Improving asthma care by reducing emergency room visits and avoidable hospitalizations (i.e., hospital admissions that might have been avoided with high quality ambulatory care) should have a substantial impact on Medicaid spending. Estimating potential Medicaid savings from asthma disease management—a Virginia example. Below are steps for estimating the Medicaid savings from training physicians in the Virginia Health Outcomes Partnership program described earlier. Estimates for Virginia are below. Using these steps together with State data, it is possible for a State to develop a “ballpark” estimate of how much might be saved in Medicaid costs with a similar asthma disease management intervention.

Steps for Estimating Potential Medicaid Savings From an Asthma Disease Management Program

Step Virginia 1 Total annual spending for emergency department visits for asthma pre-

intervention for Medicaid recipients

$5,056,020

2 Total annual number of Medicaid claims for emergency department visits 9,363

3 Payment per claim: Divide step 1 by step 2 (5,056,020/9,363) $5404 Emergency visit reduction factor: Adjusted to four quarters and to exclude

added costs per physician and added drug prescribing (both included below; see steps 7 and 8)

0.06

5 Emergency care visit annual saving after training physicians: Multiply step 1 by step 4 (5,056,020 X 0.06)

$303,361

6 Number of physicians participating in primary care case management who might accept training in asthma management

200

7 Asthma drug cost: Multiply step 6 by $180 per physician per year (200 X 180)

$36,000

8 Program training costs: Multiply step 6 by $235 per physician (200 X 235) $47,000

9 Total drug and training costs: Add steps 7 and 8 (36,000 + 47,000) $83,00010 Total Medicaid savings: Subtract step 9 from step 5 (303,361 − 83,000) $220,361

11 Savings per Medicaid claim: Divide step 10 by step 2 (220,361/9,363) $23.54

Source: Estimates derived from Rossiter et al., 2000. Note: See Rossiter et al. for further detail on derivation of the emergency visit reduction factor, asthma drug cost, and program training cost. Based on the VHOP experience, step 6 assumed that one-third of Medicaid participating physicians in any disease management program would accept training in asthma management.

People with asthma who have poor asthma management have a high number of repeat ED visits. Data from the National Medical Expenditure Survey show that only about 20 percent of all asthma patients account for about 80 percent of the total costs of asthma (Weiss et al., 2001; Smith et al., 1997). A recent study showed that from a group of more than 3,000 patients, asthma patients with 6 or more ED visits accounted for 68 percent of total ED visits (Griswold, 2005).

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If these asthma patients with multiple ED visits can be identified with State Medicaid data, then States can estimate potential cost savings from reducing the number of patients with repeat emergency room visits. Multiplying the number of patients who have different numbers of visits by the average cost per visit for each group gives an estimate of total ED costs for patients with asthma who have frequent ED visits for each group. These costs represent a potential target for reducing health care costs for patients with asthma and compare the cost of moderate emergency use to high emergency department use for asthma.

Cost of Excess Hospitalizations

Rates of avoidable hospitalizations have been developed as indicators of the quality of ambulatory care, including care for asthma. Hospitalizations occur because of exacerbations of asthma symptoms such as an asthma attack, during which a patient cannot breathe and could die without medical attention. Some asthma hospitalizations could be avoided with planned care, patient education, proper use of long-term controller medications for people with persistent asthma, and patient awareness and avoidance of asthma triggers. However, even for patients and physicians who comply with the best practices, asthma attacks beyond their control may still occur and hospitalization may be necessary for survival. It is the wide variation in asthma admissions rates across the country (see Table 1.2) that suggests considerable improvement can be made in ambulatory care and self-management that results in reduced hospitalizations and, thus, lower costs for asthma care.

A recent study found that about half of admissions for children with asthma in one hospital may have been preventable. In a Massachusetts inner-city hospital, 26 percent of parents thought their child’s hospitalization for asthma could have been avoided, 38 percent of primary care physicians thought an admission could have been avoided, and 43 percent of the inpatient attending physicians who saw a child with asthma in the hospital had that view (Flores et al., 2005). These assessments were independent of each other. The one group without a personal stake in the assessment of the chronic care of the children was the inpatient physicians with the highest assessment of avoidable admissions. Of all admissions for children with asthma, 54 percent of admissions were assessed as preventable by any of the three sources. Estimating potential cost savings from reducing excess hospitalizations for pediatric asthma—a Massachusetts validation. By comparing the Massachusetts hospitalization rate with the average for States with the lowest rate of hospitalization for children with asthma, the apparent excess (or percent to be reduced in order to achieve best-in-class performance) in Massachusetts is 60.8 percent (Table 1.2). This potential for reduction of pediatric asthma hospitalizations for Massachusetts is similar to the 54-percent estimate of hospitalizations that might have been prevented, based on the judgment of parents, physicians or attending physicians at the Boston hospital described above. This supports the use of hospitalization rates above and beyond the best-in-class States average rate as a metric to evaluate how much States could save with better quality of asthma care. Using Massachusetts as an example, the steps in the following calculation show how a State may develop a ballpark estimate of the potential cost savings from reducing excess hospital admissions for pediatric asthma. Note that the cost of implementing a quality improvement program to reduce hospitalizations is not included in the calculation.

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Steps for Estimating Potential Savings From Reducing Excess Pediatric Asthma Hospitalizations

Step Massachusetts 1. Hospital admission rate for pediatric asthma per 100,000 population

under age 18 (Table 1.2) 184.13

2. Estimated population under age 18 in State (U.S. Census, 2000; see http://www.census.gov/popest/states/asrh/SC-est2004-02.html)

1,464,189

3. Number of pediatric asthma hospital admissions: Multiply step 1 by step 2 and divide by 100,000 (184.13 X 1,464,189)

2,696.01

4. Percent of pediatric asthma hospital admissions to be reduced to achieve best-in-class (Table 1.2)

60.8%

5. Number of hospital admissions for pediatric asthma to reduce (excess hospitalizations): Multiply step 3 by step 4 (2,696.01 X 0.608)

1,639.17

6. Mean cost for pediatric asthma hospitalization* $ 3,213

7. Total cost of all pediatric asthma hospitalizations in State: Multiply step 3 by step 6 (2,696.01 X $ 3,213)

$8,662,280.13

8. Total cost of excess pediatric asthma hospitalizations in State: Multiply step 5 by step 6 (1,639.17 X $3,213)

$5,266,653.21

9. Potential cost savings from reducing excess hospitalizations: Subtract step 8 from step 7 ($8,662,280.13 − $5,266,653.21)

3,395,626.92

* 2004 HCUP Nationwide Inpatient Sample. (Information on HCUP data and tools is available on the HCUP Web site at http://www.hcup-us.ahrq.gov or via email at hcup@ahrq.gov.)

Summary and Synthesis This module provides background on asthma as a disease, its prevalence, complications, and associated costs. This module also examines the evidence from both the NHQR and NHDR regarding the substantial variation in quality of care for asthma that exists across the Nation, between States, and across population subgroups.

Evidence from research indicates that quality improvement can enhance health outcomes, reduce disparities across States and population groups, and provide a return on the investment. The return includes both cost savings and improved quality of life for people with asthma and their caregivers.

Resources for Further Reading • American Lung Association Trends in Asthma Morbidity and Mortality; available at:

http://www.lungusa.org/atf/cf/%7B7a8d42c2-fcca-4604-8ade-7f5d5e762256%7D/ASTHMA%20JAN%202009.PDFNational Asthma Education and Prevention Program-- http://www.nhlbi.nih.gov/about/naepp/

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• Institute for Healthcare Improvement Web resources, available at: http://www.ihi.org/IHI/Topics/ChronicConditions/Asthma/

• Institute of Medicine’s Crossing the Quality Chasm: A New Health Care System for the 21st Century, available at: http://www.iom.edu/report.asp?id=5432

• Institute of Medicine’s Fostering Rapid Advances in Health Care: Learning from System Demonstrations, available at: http://www.iom.edu/report.asp?id=4294

• National Healthcare Quality Report and National Healthcare Disparities Report, available at: http://www.qualitytools.ahrq.gov

• National Heart, Lung, and Blood Institute’s Morbidity and Mortality: 2004 Chartbook on Cardiovascular, Lung, and Blood Diseases; available at: http://www.nhlbi.nih.gov/resources/docs/04_chtbk.pdf

• Boudreaux ED, Emond SD, Clark S, et al. Acute asthma among adults presenting to the emergency department: The role of race/ethnicity and socioeconomic status. Chest. 2003;124:803-812.

• Griswold SK, Nordstrom CR, Clark S, et al. Asthma exacerbations in North American adults: Who are the “frequent fliers” in the emergency department? Chest. 2005; 127(5):1579-1586.

• Lin S, Fitzgerald E, Hwang S et al. Asthma hospitalization rates and socioeconomic status in New York state (1987-1993). Journal of Asthma. 1999;36:239-251.

• Mayo PH, Richman J, Harris HW. Results of a program to reduce admission for adult asthma. Annals of Internal Medicine. 1990;112:864-871.

• Ray N, Thamer M, Fadillioglu B, et al. Race, income, urbanicity, and asthma hospitalization in California: a small area analysis. Chest. 1998;113:1277-1284.

• Stanton MW, Dougherty D, Rutherford MK. Chronic care for low-income children with asthma: strategies for improvement. Rockville (MD): Agency for Healthcare Research and Quality; 2005. Research in Action Issue 18. AHRQ Pub. No. 05-0073.

• Zeiger RS, Heller S, Mellon MH. Facilitated referral to asthma specialist reduces relapse in asthma emergency room visits. Journal of Allergy and Clinical Immunology. 1991;87:1160-1168.

• Zoratti E, Havstad S, Rodriguez J et al. Health service use by African Americans and Caucasians with asthma in a managed care setting. American Journal of Respiratory Critical Care Medicine. 1998;158:371-377.

Associated Appendixes for Use With This Module

Appendix A: List of Acronyms Appendix A lists acronyms of organizations, data sources, and other resources used in this Resource Guide.

Appendix B: Estimates of Medicaid Costs by State

Appendix B includes data tables with the cost estimates for racial/ethnic subgroups of Medicaid eligibles with asthma by State and a flow chart of the methodology used to derive the estimates.

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Module 2: A Framework for State-Led Quality Improvement States can play a central role in improving the quality of health care for their residents. This module presents a framework to help States play this role.

Quality Health Care and the Quality Improvement Movement Health care quality has been defined as “the degree to which health services for individuals and populations increase the likelihood of desired health outcomes and are consistent with current professional knowledge” (Institute of Medicine [IOM], 1990). Increased attention to quality of care in recent years has highlighted the gap between optimal health care and the care that Americans typically receive. While producing unrivaled innovation and new medical treatments, the U.S. health care system struggles to deliver high quality care consistently. Researchers estimate, for example, that nearly 100,000 people die annually in the United States because of medical errors (IOM, 1999). And even when fatal errors are not involved, people receive appropriate treatment only about half of the time (McGlynn et al., 2003).

Compared with other industries in the United States, health care has been slow to embrace quality improvement (Chassin, 1998). By contrast, some manufacturing- and service-based industries have implemented sophisticated and rigorous quality improvement processes, such as the Six Sigma movement adopted by large firms including Motorola and General Electric. This movement is named for its goal, “six sigma,” which refers to a measure of extremely low tolerance for mistakes. Specifically, six sigma represents 3.4 defects per million events (Spanyi and Wurtzel, 2003). The Six Sigma approach thus sets a very ambitious goal for reducing error. Health care processes typically operate at a considerably higher tolerance for error—500,000 defects per million opportunities (based on the conclusion of McGlynn et al., 2003)—or less than two sigma rather than six.

Key Ideas in Module 2: • States can play a strategic role in designing, implementing, and assessing health

care quality improvement. • Existing models for quality improvement can be adapted to enable States to play a

leadership role. • The State-led framework is adapted from quality improvement models in other

industries and incorporates a Plan-Do-Assess approach.. • The State-led quality improvement framework contains three stages:

1. Provide leadership to create a vision. 2. Work in partnership with key stakeholders. 3. Implement improvement by creating interventions and assessing their impact.

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One of the obstacles to quality improvement in health care has been a lack of rigorous measures and data to drive improvement. To help address this gap, the Agency for Healthcare Research and Quality (AHRQ) released the first National Healthcare Quality Report (NHQR) and National Healthcare Disparities Report (NHDR) designed to establish a baseline of quality measures for tracking health care quality in the United States in 2003. The second NHQR and NHDR were released in 2004 and began to track health care quality. The 2004 NHQR concluded that quality is improving in many areas, but change takes time, the gap between the best possible care and actual care remains large, and further improvement in health care is possible (AHRQ, 2004b). In addition, AHRQ has begun to develop resource guides and workbooks aimed toward helping States take action to improve quality of care for specific chronic conditions (e.g., diabetes and asthma). These and other resources from AHRQ and other Federal agencies designed to stimulated quality improvement are listed at the end of this module.

A Strategic Role for States

Improving quality of care requires active involvement from many participants—providers, patients, payers, policymakers, and the public. Among all of these stakeholders, however, State governments have a unique leadership role to play. Small networks of providers have developed around quality improvement, but strong leadership at the State level is needed to help these develop, coalesce, and survive. States also have a span of control over a network of providers that they license and can help integrate the efforts of the various networks. Furthermore, they have the ability to lead providers in developing a quality improvement process and can muster a statewide impetus behind small efforts that might otherwise die for lack of energy. Some parts of State government stand to benefit from quality improvement in terms of improved services and lower costs. These include Medicaid agencies and mental health and substance abuse agencies that also control payments to providers. To lead a quality improvement effort, States need a model of how to improve quality and a way to target areas for improvement These are discussed in more detail below.

Developing a Framework for State-Led Quality Improvement

None of the current models for quality improvement used on the front lines of medical care addresses a strategic role for State governments. Therefore, this Resource Guide proposes a State-led quality improvement approach that combines general models from product manufacturing with specific models developed for health care services. Advocates of quality improvement have argued that a quality improvement model adapted from manufacturing can work just as well in service industries (Harry 1998, as cited in Chassin 1998). Various quality improvement models used in different circumstances are discussed below; then a State-led framework, built by borrowing from other models, is presented. General Models General models of quality improvement are based on the “Plan-Do-Check-Act” or the “Plan-Do-Study-Act” (PDSA) model (Langley et al., 1996). Within a production process, these models convey the importance of the following:

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• Planning—Identifying the problem and potential solution. • Doing—Actually testing out the proposed solution. • Studying—Measuring to see if the solution worked. • Acting—Implementing the successful solution. Two key features of this model are measurement and the continuousness of the process. Organizations measure the effect of a change to know whether the solution is working. A familiar mantra in quality circles is: “Without measurement, there can be no improvement.” If the test solution did not work, the group starts again to plan a better approach, do another test, and assess its effect. Businesses apply this continuous process of planning, doing, and assessing until they know they have solved a problem. Then they implement the solution company wide. Although this model has stood the test of time in manufacturing circles, it requires special application in health care. Unlike centrally controlled manufacturing processes, health care delivery is decentralized and resistant to top-down directives from government, corporate decisionmakers, or professional organizations. Health care quality improvement happens in clinical settings, often one patient at a time. The decentralized nature of health care delivery thus creates a substantial obstacle to implementing large-scale quality improvement programs. In light of this fact, the components of the process must be carefully adapted to the health care setting. Three components in particular that need special attention are the composition of the team, the plan for measurement and assessment, and the implementation process. First, the quality improvement team is as crucial to success as the process. This is true in companies that compose their teams of knowledgeable and empowered employees, but more so within complex systems of disconnected entrepreneurs, such as in medicine. Highly effective teams are committed to the process, champion the cause, apply their energy to implement solutions, and continue the quality improvement cycle by moving on to the next problem. Achieving this in health care can be particularly challenging. A State’s leadership can influence the composition of the team. Second, the plan for measuring and assessing which proposed solutions are likely to work requires data collection, careful analysis, and skillful interpretation. While the quality improvement objective should be paramount and data and analysis should not paralyze the quality improvement team, the complexity of the health care system will present challenges to measurement and assessment. Fragmentation of the health care system, financial incentives that can discourage change, busy practitioners who may believe they have little time for quality improvement, and solo practice or employment arrangements that promote practitioner independence are special challenges to instituting change. A State’s experience around data collection can be an important asset to the team Finally, while implementation within the walls of a manufacturing plant may be straightforward, implementation in a complex health care environment may not be. Thus, the plan-do-study part of the cycle may be needed to help implement change – plan the change, measure its spread, and assess its impact on the goal. A State’s involvement may be essential to advertising and assessing the effect of specific interventions statewide.

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Existing Clinical Models The general PDSA model has been applied successfully to the delivery of health care services. These applications have focused primarily on clinical processes—i.e., how health care teams of physicians, nurses, technicians, managers, and others change specific processes to improve the outcome of their service and the health of their consumers, the patients. These applications have generally focused on one clinical condition (e.g., diabetes) or one set of procedures (e.g., anesthesia services) at a time. The clinical condition or procedure focus is an aspect of clinical models that will likely be reflected in State-led quality improvement circles. Furthermore, the clinical quality improvement process may be used within the State-led quality improvement initiative. Institute for Healthcare Improvement. One approach to quality improvement with relevance for State-led efforts was developed by the Institute for Healthcare Improvement (IHI). The IHI has been working with teams of clinicians from around the country for several years on improving systems of care to enhance care processes and outcomes. IHI has developed a two-part model to spur improvement in clinical settings (see box ). Chronic Care Model. Another model of quality improvement in the clinical setting is the Chronic Care Model. Dr. Edward Wagner and his team at Group Health Cooperative in Seattle, with support from the Robert Wood Johnson Foundation, developed the Chronic Care Model (see box). The U.S. health care system is oriented more toward care for acute episodes of disease rather than prevention and management of chronic conditions. Thus, the Chronic Care Model emphasizes a collaborative approach among health care teams, involved patients, and supportive communities to develop new and better clinical procedures and systems that support treatment and management of chronic illness over time. More information is provided below on involvement of State health departments in Diabetes Collaboratives that use the Chronic Care Model to achieve rapid advancement in diabetes care at community health centers. More information on the Chronic Care Model is available on the Improving Chronic Illness Care (ICIC) Web site at: http://improvingchroniccare.org. Federal models. None of these models speaks directly to the Federal role in promoting quality improvement. To fill the need for such a model, the Centers for Medicare & Medicaid Services (CMS) published its own quality roadmap—a strategy for how CMS plans to lead quality improvement at the clinical level for its beneficiaries (CMS, 2005). The CMS Quality Improvement Roadmap vision is: “The right care for every person every time.” Its goals are to: “Make care safe, effective, efficient, patient-centered, timely, and equitable.” The strategy, in brief, is to: • Work with partnerships to achieve quality goals. • Support quality measurement and information. • Create the right incentives by paying for quality, not ineffective, health care. • Assist practitioners to improve quality. • Drive better use of effective health care technologies.

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The Center for Medicaid and State Operations (CMSO) announced a quality initiative for Medicaid and the State Children’s Health Insurance Program (SCHIP) in August 2005 that is committed to the vision of the CMS Quality Improvement Roadmap for Medicaid and SCHIP beneficiaries. The initiative stresses the importance of working in partnership with States and external organizations, such as AHRQ, to promote innovation as a strategy for obtaining the best value for health care resources invested. The Medicaid SCHIP quality initiative includes a series of projects in five key areas: namely (1) evidence-based care and quality measurement, (2) pay for performance, (3) health information technology, (4) partnerships, and (5) information dissemination. CMSO plans to work with States to encourage Medicaid and SCHIP providers to adopt well accepted clinical guidelines with demonstrated effectiveness in improving quality and reducing costs for specific conditions in priority areas. This initiative has direct implications for States to align quality improvement and incentives to provide effective care for beneficiaries. In order to meet the objectives of the Quality Improvement Roadmap, it will be necessary for States to implement quality improvement strategies for effective care for chronic conditions such as asthma.

The IHI Methodology Part 1

Forming the team: This step involves identifying the key players and addressing three specific questions as shown below

o Setting the aims: What are the goals? o Establishing measures: How can teams measure whether a change is an

improvement? o Selecting changes: What changes can teams make that will result in improvement?

Part 2 Testing changes: This step draws from the Plan-Do-Study-Act cycle. PDSA is a way of testing a change in a real work setting—by planning it, trying it, observing the results, and acting on them.

Implementing changes: After testing changes on a small scale, learning from the tests, and refining the change through several PDSA cycles, the team can implement the change on a broader scale—for example, for an entire pilot population.

Spreading changes: After implementation of a change for a pilot population, the team can spread change to other parts of the organization or to other organizations.

For more information, see http://www.ihi.org/IHI/Topics/Improvement/ImprovementMethods/HowTo Improve/.

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A New Framework for State-Led Quality Improvement This Resource Guide proposes a new tool for State leadership in quality improvement. The State-led framework draws elements from the models described above. It overlays the PDSA model, which here is shortened to Plan-Do-Assess for States because they are not in a position of actually changing clinical practice but rather of leading others to improve. States can play a central role at three different stages of quality improvement: leading, partnering, and implementing improvement. Each stage follows the Plan-Do-Assess cycle with an emphasis on measurement and information which States may be in a unique position to support. Each stage is described in more detail below. Stage 1: Provide leadership. Figure 2.1 depicts the first stage of quality improvement—leadership. State government is the principal player at this stage. The State’s leadership role can be built with the aid of the Plan-Do-Assess tool. For example: Specific leadership tasks can be addressed with this framework. For example, an early question will be how a State official would initiate a quality improvement project. With the PDA tool, the State official would:

• Plan—A State official leads the process by assigning high-level staff who identify partners from among stakeholders and prepare for a kickoff meeting by collecting and assembling data—the case for quality improvement, potential targets for improvement, readily available data across clinical conditions or settings of care.

• Do—Staff convene partners, a high-profile State official kicks off the meeting, and staff support the partners in a planning process.

• Assess—Staff assess the partnership (for example, who is attending and contributing at meetings) and adjust the partner membership, if necessary.

Chronic Care Model—The Six Core Components

Community - Mobilizing all the available community resources to meet the needs of people with chronic illnesses.

Health system – Creating organizational cultures, systems and mechanisms that promote safe, high quality care throughout the health care system.

Self-management support – Empowering and preparing active patients to manage their health and navigate the health care system.

Delivery system design – Assuring the delivery of effective, efficient clinical care and self-management support through appropriate design of the delivery system.

Decision support – Promoting appropriate clinical care consistent with scientific evidence and patient preferences.

Clinical information systems – Organizing patient and population data to facilitate efficient and effective care for people with chronic illnesses.

Source: Bodenheimer T, Wagner EH, Grumbach K. Improving primary care for patients with chronic illness: the chronic care model, Part 2. Journal of the American Medical Association. 2002;288(15): 1909–1914.

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A key component of State government leadership at this stage is championing the need for quality improvement. It also is critical to State efforts to identify one or more high placed champions from the health care community who can muster support and provide a vision for change. Figure 2.1. State-led quality improvement—Stage 1

Stage 2: Work in partnership. Figure 2.2 adds the second stage of State-led quality improvement, as a ring surrounding the first circle (see Figure 2.1). The second ring focuses on the partnership activities, encompasses the core of the improvement process, and relies heavily on the partnership to define activities, plan solutions, and assess them before any implementation campaigns are undertaken. Many issues will be decided during this stage, usually during a series of group meetings. Again, the Plan-Do-Assess tool can be used for each major decision, which at this stage might include, for example:

• Plan—The new partnership will develop a strategy about how the group will function (perhaps through consensus) and what clinical condition(s) and/or settings of care will be the focus of quality improvement, commit to both the group process and the focus, and design a plan for quality improvement and specific solutions for the condition(s) and/or settings selected.

• Do—Team members will test proposed intervention(s) through study of the literature and/or a pilot study at a health plan or facility; and during the study or test, they will measure and quantify the effect of the intervention(s).

• Assess—Team members will analyze and interpret the results of the intervention(s) and present the results to the partnership.

Depending on the results, the cycle may begin again with modification of the idea or generation of new ones and with the test, measure, analyze, and interpret steps again. Or, the group may be ready to move to implementation of the initial idea(s). These activities rely on a vibrant, committed partnership of stakeholders in the industry (the State being one stakeholder/partner) to identify health care problems, propose and test solutions,

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measure and analyze results of the test, and assess the promise of statewide implementation for improving quality. The solutions will undoubtedly include private-sector and public-sector solutions. Figure 2.2. State-led quality improvement—Stages 1 (inner circle) and 2 (outer ring)

Stage 3: Implement improvement. Figure 2.3 completes the quality improvement process by adding the third and final ring—implementing improvement. This stage is essential for spreading success. Because this is where complex partnerships might falter, this ring also uses the Plan-Do-Assess process for implementation. The activities might include:

• Plan—A written plan to spread ideas for change in public and private programs might encompass an advertisement campaign and/or new financial incentives or award mechanisms for quality improvement. The plan should specify how each partner will contribute to the process of bringing about change. A written plan is important to test and coalesce the group’s commitment, which will be essential for successful implementation.

• Do—The implementation begins as the group sets about to spread the change and measure the impact of the effort to spread change. The group could falter here by assuming the work of the group is finished. However, the measurement step at this point is key to determining whether the groups’ ideas are effective, continue to be implemented, and have the desired effect.

• Assess—The group should reconvene periodically to evaluate and discuss the spread of change and its outcome—successes and failures. It may be necessary to modify the plan and try new approaches, continuing to measure and evaluate the modifications. Or, the group may be ready to tackle the next area for improvement.

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Here, the continuous cycle of quality improvement is apparent. The team identifies areas in need of improvement, designs a solution, tests it, and plans how to move the solution beyond a specific demonstration setting and into the broader practice of health care. The team also measures the spread and uptake of the solution in practice, assesses whether the spread has been successful and tackles the next problem area. An effective team is committed to ongoing quality improvement. Figure 2.3. Complete State-led quality improvement framework—Stages 1 (inner circle), 2 (middle ring), and 3 (outer ring)

The complete framework. The process of quality improvement may take more than a single turn around the circles before results are seen. Furthermore, it will require continuous application of the framework to specific quality problems to improve health care quality statewide. This means that commitment, leadership, continuity, and the right incentives are essential. States, as health care purchasers and leaders in health policy, are well positioned to provide these characteristics. In this framework, the State is the supporting structure that brings the partnership together and nourishes it. State leadership provides energy, facilitates group processes, supplies data when available and may collect new data, disseminates evidence-based information (or asks another partner to assume that role), and stimulates the group to improve health care quality. The State provides an environment for competitors to come together and improve their professional services. As noted earlier in the discussion of general models, many local and regional quality improvement efforts already exist among disconnected groups, and thus a critical aspect of the State role will be outreach and education to coordinate and harmonize these diverse efforts.

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Selecting Targets for Improvement Improving any process requires targeting specific areas or problems. In health care, specific conditions or treatments are usually the place to start. Deciding which health conditions or procedures to select for improvement is the first challenge facing State leaders and their partners. Finding candidates for improvement will be relatively easy; narrowing the list will be difficult. Various criteria can be used to identify targets. Answers to a series of questions can help determine a priority list of targets for quality improvement. The quality improvement team may want to add to or subtract from these: • Is there clinical or quality improvement evidence that specific changes will improve health

care outcomes? • What measures of quality health care exist for this targeted area? • Are there benchmarks for high quality care? • Is there variation across geographic areas, vulnerable subpopulations, or individual providers

in the quality of care delivered? Is the variation excessive compared to that in underlying clinical conditions that clinicians must treat in different ways?

• Is there a way to assess how a State or smaller geographic area performs in a targeted area? • How many lives are affected by the condition or treatment (i.e., prevalence, morbidity, and

mortality related to the condition)? • What is the cost of care and the potential for a return on (or saving from) investment in

quality improvement? Most of these questions take considerable effort to answer. For this reason, AHRQ has begun to assemble a set of resources targeted to helping States implement quality improvement initiatives.

Information Resources for Quality Improvement AHRQ-Sponsored Resources for States AHRQ provides a number of resources for information and measures on health care quality. AHRQ supports research programs and publications intended to provide scientific evidence for quality improvement. Other important Federal resources on asthma are noted below also. NHQR and NHDR. Two valuable resources are the National Healthcare Quality Report and the National Healthcare Disparities Report mentioned above. The former offers benchmarks for tracking U.S. health care quality nationally and by State; the latter looks at quality of care and access to care for vulnerable subpopulations, such as racial/ethnic minority groups and low income groups. The reports, mandated by Congress, were first published in 2003 and are produced annually; to date, reports for 2003 through 2008 are available. All releases of the NHQR and NHDR can be accessed at: http://www.ahrq.gov/qual/qrdr08.htm. State resources for selected measures from the NHQR. Measures at the State level from the 2007 NHQR are available as a user-friendly, interactive Web resource for examining the performance of each State and the District of Columbia across various dimensions of quality.

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These dimensions include types of care (preventive, acute, and chronic), settings of care (hospital, ambulatory, nursing home, and home health), and total quality (a summary of all State-level measures in the NHQR). Also included are breakdowns of the measures that go into creating each summary measure. Users can also find quality measures available for specific clinical conditions in downloadable tables. These State resources are available at: http://statesnapshots.ahrq.gov/snaps08/index.jsp. HCUP and statewide discharge data systems. Another source for State-level data is the statewide discharge data developed within States by State governments, hospital associations, and other private data organizations. The Healthcare Cost and Utilization Project is a public-private partnership sponsored by AHRQ with 37 participating statewide data organizations that accounted for about 90 percent of U.S. discharges in the United States in 2004. As noted in Module 1, HCUP provides asthma hospitalization rates for participating States. More information on HCUP is available at: http://www.hcup-us.ahrq.gov. Evidence reports. AHRQ-sponsored Evidence-based Practice Centers (EPCs) review all relevant scientific literature on clinical, behavioral, and organization and financing topics to produce evidence reports and technology assessments. These products are used for informing and developing coverage decisions, quality measures, educational materials and tools, guidelines, and research agendas. EPCs also conduct research on methods of quality improvement. Topics are chosen for their relevance to clinical, social science/behavioral, economic, and other health care organization and delivery issues—specifically those that are common, expensive, and/or significant for the Medicare and Medicaid populations. There are over 170 evidence reports assessing various clinical issues (including asthma) and other topics, such as approaches for closing the gap in health care quality. A list of these reports is available at: http://www.ahrq.gov/clinic/epc/epcseries.htm. Quality improvement tools. AHRQ also publishes resources for quality improvement for specific chronic illnesses. This updated Resource Guide is the second published by AHRQ to focus on a specific chronic illness for States. Diabetes Care Quality Improvement: A Resource Guide for State Action and Diabetes Care Quality Improvement: A Workbook for State Action were published in 2004. These resources provide measures and benchmarks for States to develop their own quality improvement goals and strategies in addition to the ones provided in the National Healthcare Quality and Disparities Reports. Copies of the diabetes Resource Guide and companion Workbook can be downloaded at http://www.ahrq.gov/qual/diabqualoc.htm . National Quality Measures Clearinghouse™ (NQMC). AHRQ sponsors the National Quality Measures Clearinghouse™. This online clearinghouse is a database and Web site for information on specific evidence-based health care quality measures and measure sets. It provides practitioners, health care providers, health plans, integrated delivery systems, purchasers, and others a way to get detailed information on quality measures for quality improvement. The NQMC is available at: http://www.qualitymeasures.ahrq.gov/.

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Other Federal Data Resources for States Data sources that can be found within States may include disease registries, hospital discharge data programs, etc. After seeking asthma data within the State, States will need to look for national asthma data to use for benchmarking their progress. Behavioral Risk Factor Surveillance System. BRFSS is a national data source that provides data at the State level. Currently, it provides the richest source of asthma data nationwide and by State. BRFSS data are based on telephone surveys developed by the CDC but administered by each State independently. The survey consists of a core set of questions developed by CDC, additional questions developed by the States, and separate, optional modules for States to use. The asthma module, which contains the quality-of-care questions, is optional for State use. More information about the BRFSS data and methods as well as interactive databases with some State and local level asthma data are available at: http://www.cdc.gov/brfss/. National Asthma Control Program (NACP). The Centers for Disease Control and Prevention also supports a number of Federal programs for States including the National Asthma Control Program. (See Appendix C for a list of State interventions for asthma.) This program funds States to provide surveillance on asthma and other interventions. Information about the NACP can be found at http://www.cdc.gov/asthma/NACP.htm. National Asthma Survey (NAS). States should also note that the National Asthma Survey has been developed by CDC and other partners as a model for States to use to collect information on asthma prevalence and care. The NAS data set includes the BRFSS asthma measures in addition to nearly 70 other measures for asthma. (NAS measures are discussed more fully in Module 4 and in Appendix D.) A pilot data release of NAS data for four States is available at: http://www.cdc.gov/nchs/about/major/slaits/nsa.htm. National Asthma Education and Prevention Program (NAEPP). Another Federal program for States, the NAEPP works with intermediaries including major medical associations, voluntary health organizations, and community programs to educate patients, health professionals, and the public. NAEPP is coordinated by the National Heart, Lung, and Blood Institute. Part of the National Institutes of Health (NIH), NHLBI develops clinical guidelines for diagnosis and treatment of asthma. Information about NAEPP can be found at http://www.nhlbi.nih.gov/about/naepp/.

Summary and Synthesis States have typically viewed their role in quality improvement from the perspective of the guardian of public health, a manager of health care for the poor or disabled, or a buyer of health insurance for State employees. However, States can play a more comprehensive leadership role, and indeed some States are already doing this, at least in part, with respect to asthma. The framework described in Module 2 envisions three roles for States in quality improvement: 1. Provide leadership, which entails providing a defining vision for change, identifying

partners to set goals, and providing an environment that fosters improvement.

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2. Work in partnership, which involves creating a committed partnership of stakeholders dedicated to identifying and proposing and testing solutions and developing plans for improvement.

3. Implement improvement, which means creating interventions within a strong partnership, measuring and analyzing the results of changes, and applying successful improvements on a broader scale. The solutions will undoubtedly include those from both the private and public sectors.

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Module 3: Learning From Current State Quality Improvement Efforts States are currently involved in many efforts to improve the quality of asthma care. These actions can inform other State efforts. This module provides examples of current State efforts to improve the quality of asthma care within the context of a State-led model of quality improvement.

The Introduction to this Resource Guide described a new, strategic role for States in leading quality improvement for asthma. A State-led quality improvement framework that States could use in playing this role was described in Module 2. Building on lessons from industrial and clinical models for quality improvement, it adapted the Plan-Do-Study-Act cycle of quality improvement for the policymaking context to a Plan-Do-Assess approach illustrated in Figures 2.1- 2.3. This framework identifies the three stages in which States can play a key role—provide leadership, work in partnership, and implement improvement. Many States are already active in these areas, so Module 3 also provides specific examples of what States are doing currently at each of the three stages presented above.

Current State Efforts To Improve the Quality of Asthma Care States have typically viewed their role in quality improvement from a public health perspective or, more narrowly, as a buyer of health insurance for state employees. However, as outlined in the introduction, States can play a broader and more strategic role. Some States are already doing this, at least in part, with respect to asthma. States have undertaken a variety of asthma initiatives over the years. Many of these have been funded by the Centers for Disease Control and Prevention. States have used CDC funding to establish creative programs to address asthma prevention and control. As attention to health care quality has increased, State asthma programs have also adopted quality improvement aims. States also initiate other programs. States have established asthma disease management programs in Medicaid and have partnered with the private sector on quality improvement for asthma care. Many States also have tried to integrate CDC-funded efforts with private sector and Medicaid efforts.

Key Ideas in Module 3:

• A variety of quality improvement initiatives at the State level are sparking change in health systems across the Nation.

• States can use this module to identify examples and resources for asthma care quality improvement.

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Appendix C lists over 100 separate programs in 48 States that target improvement in some aspect of asthma care and include efforts at the Federal and State/local levels, joint private/public efforts, and efforts by private national organizations with Internet links to more information. A significant number of these programs are targeted to specific populations—such as children, minority communities, or Medicaid recipients—or on public health approaches toward asthma mitigation. However, it is difficult to generalize about these programs, given their diversity and heterogeneity. Therefore these programs are divided into 12 categories that relate their relevance to some of the important aspects of quality improvement:

• Advisory bodies and councils. • Coalitions. • Collaboratives. • Cross-agency work. • Data measurement and reporting. • Developing and enforcing guidelines. • Disease management. • Minority and rural outreach. • Public service/education efforts. • Self-management (of asthma). • Provider training. • Use of technology.

Note that these categories cut across the three stages of activity highlighted in the State-led model. The number and range of State activities make it difficult to present all instances. Therefore this module presents some examples of specific programs in States that fit these stages. In stage 1, States are providing leadership by championing quality, convening partnerships, and providing support. In stage 2, States are working in partnership on various activities—planning for quality improvement, developing and complying with asthma guidelines, and supporting measurement and data collection. In stage 3, States are implementing improvement by supporting activities that implement asthma care quality interventions of various types, evaluating their effectiveness, and spreading success. Most of the information provided below and in Appendix C was derived from a review of State health department Web sites, CDC resources, Internet research, and in-person interviews with State agency officials. Examples below provide a sampling of State efforts that reflect regional, size, and funding differences among States. Although not an exhaustive list, it demonstrates a range of State efforts related to asthma quality improvement.

Stage 1: Provide Leadership A Champion for Quality Having a champion for quality improvement is critical to the success of any asthma quality improvement initiative. Champions provide consistent leadership, give greater visibility to

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issues, and spur others toward greater strides in quality improvement. In some cases, asthma quality improvement initiatives have received recognition and support from the highest leaders of State government. The involvement of an influential, recognizable leader, such as a governor or other high-level elected official, can enhance stakeholder engagement in the process and heighten the attention given to quality improvement initiatives in the media and within the public and private sectors. In other cases, dedicated staff within an executive agency, such as the asthma control program staff in the State’s health department, are instrumental in providing the leadership needed to pull together diverse stakeholders and influential elected officials to promote quality. For example: • In New York, Governor George Pataki championed improving asthma care. Following

his 1999 State-of-the-State speech, New York launched an aggressive asthma prevention and control agenda. New York has developed clinical treatment guidelines for asthma, provided funding to regional coalitions to improve asthma care, and implemented a Medicaid disease management and quality improvement initiative related to asthma.

• Staff in Oregon’s Asthma Program established a Workgroup on Improving Asthma Care

with representatives from health plans, health care providers, medical professional groups, and advocates. Through a consensus process, the workgroup developed guidelines for asthma care in the State based on NHLBI guidelines that were then published and distributed by Oregon’s Asthma Program to providers, medical professionals, and others.

Convene Partners and Develop Support Quality improvement leaders cannot accomplish their task alone. Creating networks of support has been critical for State programs that address asthma quality improvement. States have used various methods to convene parties interested in improving asthma care, including creating or assembling broad coalitions or networks of multiple stakeholders as well as using advisory bodies, councils, or State workgroups that are smaller and authorized by statute or regulation. State advisory bodies, councils, and workgroups. A number of States have established through legislation or executive action, advisory boards, councils, or workgroups on asthma that assist with statewide asthma planning and quality improvement efforts. Advisory bodies, councils, and workgroups are generally led by State officials and typically include a variety of experts and stakeholder groups from the public and private sectors, such as the American Lung Association, State health professional associations, hospital associations, and provider organizations. Other stakeholders may include large businesses, employer groups, and other community leaders. These advisory bodies, usually housed within the State’s health department, are often supported through CDC Asthma Prevention and Control Programs. • The Minnesota Department of Health developed the Commissioner’s Asthma Advisory

Workgroup to provide direction and assistance in forming a statewide plan to address the rising health and economic burden of asthma in Minnesota.

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• Connecticut established its Asthma Advisory Council in 2003 to assist in the implementation of the State asthma plan. The council consists of 15 members appointed by the State Commissioner of the Department of Public Health.

Coalitions and networks. Coalitions and networks are broad-based, voluntary efforts, in contrast to advisory bodies or other State-sanctioned entities. These groups are generally formed by private initiative; elected State officials or executive agency staff may participate. Coalitions and networks bring together a broad variety of stakeholders in a State to work together to identify areas of strength, common objectives, and gaps in services. They also develop plans to assure that the essential treatment and educational services for managing asthma are in place in a community. Coalitions may also include community representatives and nontraditional partners such as the corner grocery store owner, faith communities, health organizations, social service agencies, and more. Coalitions and networks can be important allies in quality improvement efforts in the State because of their broad membership and natural interest in improving the quality of care for asthma. For example: • Ohio’s Asthma Coalition is an association of medical and public health professionals,

business leaders, various government agencies, community activists, and others dedicated to improving the quality of life for people with asthma through information sharing, networking, and advocacy.

• The Colorado Asthma Coalition is a group of health care professionals and community members committed to working together to improve public awareness and education, data collection and research, and provider education.

Stage 2: Work in Partnership Planning for Quality Improvement Convening a State advisory body or a coalition of stakeholders or both is just the first step in the improvement process. The group must then develop a strategy and plan of action for asthma quality improvement. In many States, the State’s asthma plan will be the guiding document for group action. A State asthma plan is required by the CDC for States that receive funding from its National Asthma Control Program. These plans provide an overview of the asthma prevention and control issues within the State that need to be addressed. The plan also articulates goals and identifies strategies the State will use to achieve the goals. Thirty-five States received fiscal year 2004 funding from CDC for asthma prevention and control. (More information on State asthma plans is available on the CDC Web site at www.cdc.gov/asthma. The CDC Web site provides links to State asthma programs and their State plans.) State asthma plans generally include most of the traditional public health activities such as education and awareness, data collection, disease surveillance, and partnership activities with

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State and community groups. Some State asthma plans also include improving the quality of care for asthma, although this varies. States have used the State asthma plan process to develop evidence-based asthma treatment guidelines, collect data on quality of care measures, improve asthma self-management education and practice, and train providers on asthma management training. In other cases, asthma quality improvement initiatives may develop apart from the State’s asthma program, such as with Medicaid disease management or pay-for-performance initiatives. Provided below are examples of different ways that States have worked in partnership with others to develop and implement asthma quality improvement plans. Developing and Complying With Asthma Guidelines To help translate research-based evidence into practice, several States are promoting the use of evidence-based clinical guidelines for asthma care. Like Oregon, many States have adopted guidelines established by the National Heart, Lung, and Blood Institute, while others have worked through the process of developing State-specific asthma treatment guidelines. • New York released its Clinical Guidelines for the Diagnosis, Management, and Evaluation

of Adults and Children with Asthma – 2003 along with a call to action to the State’s health care professionals to participate in regional conferences for physician education.

• Missouri’s Center for Asthma Treatment at Children's Mercy Hospital has worked to fully

implement the 1997 NHLBI guidelines for diagnosis and management of asthma by creating an integrated program for asthma treatment and standardizing the education and medical treatment of people with asthma.

• The Texas Medicaid Managed Care Asthma Project is a pediatric asthma pilot program for

Medicaid enrollees. The program provides standardized patient and family asthma management education and supplies best practice guidelines to providers.

Quality Measurement and Data Collection The development of quality measures and data collection and analysis are fundamental steps in quality improvement. States have used CDC asthma program funding to improve data collection, including information about prevalence, death rates, and other statistics. Other data sources include managed care plan or Medicaid program data. (More information on identifying and using asthma data sources for quality improvement plans is presented in Module 4.)

• The Colorado Asthma Program is developing a statewide surveillance data system to

determine the prevalence, mortality, and morbidity of asthma in the State and to assess any associated morbidity and mortality.

• Wisconsin’s Asthma Plan includes objectives to use (by 2007) the NHLBI guidelines for

diagnosis and management of asthma statewide and to build the capacity of health care organizations in the State to monitor and measure asthma care quality.

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Partnerships Beyond Health Care Effective asthma interventions can leverage partnerships beyond the health care setting. Asthma interventions can involve leaders in businesses, employer groups, schools and day care centers, and organizations of caretakers, social workers, and others. Comprehensive asthma interventions should address environmental issues that affect people with asthma and support self-management for patients in the context of their communities and daily lives. • The National Cooperative Inner-City Asthma Study Intervention is a social-worker-

based education program that focuses on environmental control. Social workers are trained as asthma counselors and work with the child’s caretaker to improve communications between family and physician (Sullivan et al., 2002).

Stage 3: Implement Improvement Implementing Asthma Care Quality Interventions States use a variety of interventions to affect asthma care. Some examples of ways that States seek to improve the quality of care for asthma are listed below. Self-management/patient education. Patient self-management is critical for good asthma outcomes. Patient education programs can be conducted in a variety of settings that are accessible to target populations, including: churches, neighborhood associations, schools, and community-based organizations that are well recognized in a community. These programs can be conducted in small groups, or one-on-one, based on the identified needs of the population. • Alabama’s Inner-City Asthma Intervention program at the University of Alabama at

Birmingham provides patients with individualized treatment plans and education based on evaluation from physicians, nurses, and educators to improve self-management skills.

• In North Carolina, the Inner-City Asthma Intervention program provides individualized and group educational sessions on asthma for children. The sessions provide a basic understanding of asthma, its triggers, environmental control, warning signs, and medications.

Collaboratives. Improving the quality of care for asthma is a systemic issue. The entire health care system and all its actors need to be mobilized to improve the quality of care received by persons with asthma. Building on the successes of the Health Disparities Collaboratives funded by the Health Resources and Services Administration (HRSA), a number of States have started or participated in collaboratives that bring together teams of practitioners to develop quality improvement strategies for clinical settings. Collaboratives typically use a PDSA model to bring together teams over a period of time, develop an improvement idea, test it on a limited basis, study the effect, and then implement the change more broadly.

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• California Medi-Cal officials and a group of health plans, providers and community-based organizations that serve the Medi-Cal population have participated in the Plan/Practice Improvement Partnership, a quality improvement effort aimed at developing clinical and administrative approaches to improve asthma care. The collaborative is funded through the Center for Health Care Strategies Best Clinical and Administrative Practices program.

• The New York State Medicaid Asthma Disease Management and Quality Improvement

Initiative promotes disease management interventions in the treatment of asthma. Community Health Centers in the greater NYC region provide patient education to improve health outcomes for Medicaid recipients.

Provider training. Because health care providers are a key element in improving asthma quality care, many States have actively sought their involvement in developing programs. In addition, States are providing outreach, training, and support to health care professionals as they seek to implement new evidence-based care guidelines. • Arkansas Asthma Coalition offers primary care physicians, health care providers, school

nurses, and physician office staff training that spotlights diagnosis, evaluation, and treatment of asthma as well as skills for managing, educating, and communicating with asthma patients. In addition, approximately 1,200 staff of public schools receive the American Lung Association's Asthma In-Service Training.

State disease management programs. Because States are looking for ways to control Medicaid costs while maintaining or improving quality, 38 States are implementing disease management programs, many of them targeting asthma. Medicaid disease management programs seek to increase patient knowledge and self-management skills, improve provider adherence to clinical guidelines, and implement computer technology to track patients more effectively in clinical settings for provider awareness and for system-wide evaluations of the effectiveness of the intervention. Improved care management for asthma helps patients get their asthma under control and ensures that care provided meets accepted standards. • The Virginia Health Outcomes Partnership established a training program for physicians in

the Medicaid Primary Care Case Management program that focused on reducing emergency care for asthma through better education for physicians on disease management and communication skills. The program resulted in overall savings for Medicaid, even when the training costs and higher drug costs were factored in.

• The Florida Agency for Health Care Administration’s Medicaid Disease Management

Program has contracted with experienced disease management organizations to provide disease management services to Medicaid recipients who have been diagnosed with asthma.

• The Indiana Chronic Disease Management Program (ICDMP) was developed after

legislation required the Office of Medicaid Policy and Planning to implement a disease management program for people with asthma and other chronic diseases. The ICDMP provides information on asthma for Medicaid recipients as well as all other patients.

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• The Missouri State Medicaid Disease Management program is targeted toward patients enrolled in the fee-for-service Medicaid program. This program focuses on disease management tactics for asthma patients determined to be at high risk for adverse outcomes. The goal is to slow the progression of asthma and avoid medical crises.

Pay-for-performance initiatives. Quality improvement experts have long recognized that providers have little incentive to improve health care quality in an environment where every health care organization is paid for quantity of services rather than quality. While the effectiveness of pay-for-performance on quality improvement is still being studied, an increasing number of private and public payers are exploring use of financial incentives to spur quality improvement (Dudley, 2005). CMS is conducting a number of Medicare demonstration projects that include pay-for-performance. As of January 2006, CMS had a new voluntary program on quality measure reporting to help providers assess their performance in anticipation of the trend to implement pay-for-performance in both the public and private sector. Some private insurers have already begun implementing pay-for-performance to improve quality, including Blue Cross/Blue Shield, Wellpoint Health Networks, and others. (More information can be found at The Leapfrog Group Web site at http://www.leapfroggroup.org/leapfrog_compendium.) States, too, are implementing pay-for-performance initiatives, particularly through Medicaid managed care contracts. Pay-for-performance initiatives are still in early stages of development. Currently, most pay-for-performance initiatives have a broad focus and use quality measures for several chronic conditions including asthma. • Iowa and Massachusetts have included financial incentives to contractors that deliver

behavioral health services. One study found that the contractors involved in pay-for-performance initiatives showed improvement in the specific areas where financial incentives were provided (Dyer et al., 2002).

• Medical groups in California have agreed to use common data and performance measures

for their quality improvement incentives. The pay-for-performance initiative is a collaboration of seven California health plans which use the same survey instrument for patient satisfaction and some HEDIS® measures for cancer screening, asthma, diabetes, coronary artery disease, and immunizations. More information on this initiative can be found at http://www.iha.org.

Evaluating Effectiveness Interventions to improve quality of care need to be evaluated for clinical effectiveness as well as cost effectiveness. State leaders, however, need a clear definition of success. Demonstrating real cost savings in a short time frame can be difficult. What may be more readily demonstrated is improved quality of care, cost avoidance, and improved quality of life for patients. With diligence, careful planning, and longer time to evaluate a program, States can also evaluate the cost effectiveness of quality improvement efforts. One study (Rossiter et al., 2000) offers a good example of how to design and evaluate an asthma intervention program. The Virginia Health Outcomes Partnership targeted low income asthma

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patients in a Medicaid primary case-management program. The program aimed to reduce the rate of emergency care visits for asthma. An intervention group and a control group were used to assess the effectiveness of the intervention in a real world setting where other factors can be expected to change. The study provided statistics on the reduction in emergency care visits as well as the projected direct savings to Medicaid that accounted for the cost of the physician training and the costs of increased prescribing of drugs to control the asthma symptoms. (See Module 1 for information about estimating potential cost savings from quality improvement.) Spreading Success The experience of success should not be isolated to a single program or clinic. Successful programs and strategies need to be disseminated in order for quality of care to be improved overall. It is important to adapt existing models for quality improvement and to spread their success to other communities and health care settings. • The federally sponsored Health Disparities Collaboratives, developed by HRSA with the

Institute for Healthcare Improvement, aim to transform the delivery of care in community health centers. The program improves the care for certain chronic conditions by targeting providers, patients, and communities to support provider-patient partnerships. Participants in the Health Disparities Collaboratives have worked to improve care for their patients with asthma and spread the changes and improvements throughout their health center.

• Organizations such as the National Initiative for Children’s Healthcare Quality (NICHQ)

have formed other learning collaboratives to spread success in asthma care improvement as well (http://www.nichq.org/nichq). States can use this model with other programs to spread the success of these programs.

• In North Carolina the Center for Children’s Healthcare Improvement (CCHI) has worked

with the State’s Division of Medical Administration and Area Health Education Centers (AHECs) on a variety of quality improvement projects aimed at improving the care for asthma among the Medicaid population. Building on its past successes working with clinical practices, CCHI plans a multi-tiered policy, community, and clinical approach to spread quality improvement statewide, beginning first in two AHEC regions.

Summary and Synthesis Successful programs of current asthma quality improvement activities provide State leaders with examples, useful resources, lessons learned, and approaches for enhancing initiatives and partnerships. State programs have been successful in improving asthma care. Even so, much remains to be done. States have a unique role to play in championing improvement for asthma care, forging partnerships to address approaches to change, and implementing those approaches to help providers deliver the best care and to help people with asthma enjoy optimal quality of life. Whether a State is building the infrastructure for improving asthma care or already has a well developed set of partnerships in place, there are a variety of approaches in place that can inform State efforts.

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Resources for Further Reading • Institute for Healthcare Improvement stories for asthma; available at:

http://www.ihi.org/IHI/Topics/ChronicConditions/Asthma/ImprovementStories/. • Rossiter LF et al. The impact of disease management on outcomes and cost of care: A study

of low-income asthma patients, Inquiry. 2000;37:188-202. • Rust GS, Murray V, Octaviani H, et al. Asthma care in community health centers: A study by

the Southeast regional clinicians’ network. Journal of the National Medical Association. 1999;91(7):398-403.

• Stanton MW, Dougherty D, Rutherford MK. Chronic care for low-income children with

asthma: strategies for improvement. Rockville, MD: Agency for Healthcare Research and Quality; 2005. Research in Action Issue 18. AHRQ Pub. No. 05-0073.

Associated Appendix for Use With This Module Appendix C: National and State Asthma Programs Appendix C lists asthma quality improvement programs by State and Web site links for further information.

Module 4: Measuring Quality of Care for Asthma This module discusses the basic building blocks of quality improvement—measures and data. The module describes the asthma-related data available in the NHQR and NHDR and from other sources that States can use. Each State has a cadre of health statisticians and analysts who should be recruited as part of any quality improvement project aimed at the health care system in the State because they will be familiar with local health data and because they know how to use and interpret data.

Key Ideas in Module 4:

• Quality improvement begins with measurement, which requires good measures and data for measuring quality of care.

• Process and outcome measures should be considered together to assess asthma care quality.

• The NHQR is a starting point for accessing consensus-based measures. Although a consensus on a small core of key asthma measures has not yet evolved, this Resource Guide identifies measures that are available for local quality improvement programs.

• Before undertaking any extensive data collection, State agencies should identify the questions to be answered and the data available to answer them. There are national and local data sources that can provide relevant data for creating estimates of State performance.

• State-level baseline estimates for asthma care afford State leaders a broad view of asthma care quality in their State.

• Analysis of data can answer some key questions for States: o What measures should be used to set goals for quality asthma care? o What goals should be set as targets for specific measures? o What factors influence a State’s position among other States?

Quality Measurement This section reviews the concept of quality measurement, available asthma-related measures in the NHQR and other sources, and the importance of using multi-dimensional measure sets. All of this is examined from the perspective of States and their role in initiating quality improvement programs. The Concept of Quality Measurement

The Institute of Medicine defines health care quality as “the degree to which health services for individuals and populations increase the likelihood of desired health outcomes and are consistent with current professional knowledge” (IOM, 2001). That definition suggests a distinction between quality measures and guidelines for quality care:

• Quality measures relate to populations. They include rates that indicate how many members of a population achieved a goal (for example, low emergency room visits for asthma nationwide) relative to a population base (for example, all people with asthma in the United States).

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• Guidelines for quality care are recommendations devised via consensus processes of clinical experts that describe standards of care for individual patients. In general, guidelines for individual patient care prescribe what clinicians can do to improve the care that they deliver to their patients with a specific disease or condition. These guidelines also are used as the basis for developing population-based measures that enable analysts to assess and track change in the treatment of a population.

With a specific population in mind, a quality improvement program should consider the dimensions to be measured before embarking on data collection. What is to be measured? What change will be instituted? What quality measure will track the spread of that change? What is the ultimate outcome to be improved and how is that changed measured? What special populations are to be targeted and how will their improvement be documented? Types of Quality Measures Quality measures cover a large range, from crude measures (for example, unadjusted mortality rates) to more refined measures (for example, percent using asthma medications to achieve better asthma control). Although a full range of measures is essential for a complete picture of health care quality, specific process measures are needed to guide a health care team in improving quality of care. For example, the number of deaths related to asthma at a hospital can suggest poor quality of treatment at that hospital and in the community, but knowing the number of deaths does not tell the hospital staff or community providers how to improve. Metrics that measure processes of care that reduce deaths or improve other medical outcomes help medical staff know how to change care so that they provide better care. Most quality improvement efforts focus on two types1 of measures—process and outcome:

• Process measures often reflect evidence-based guidelines of care for specific conditions. Process measures are generally considered to be within the control of the provider and, therefore, are performance indicators. They also are more likely to reveal actions that can be taken to improve quality (for example, whether a necessary test or medication is given).

• Outcome measures frequently relate to patient health status. Better outcomes are the ultimate objective of quality improvement—for example, lower mortality, lower hospitalization rates, or better test results.

Ideally, improvements in processes yield improvements in associated outcomes, and measures should reflect that. However, the connections may not be that direct. For example, the asthma process measure for inhaled corticosteroid use is included in the NHQR because the evidence-based NHLBI clinical guidelines for asthma care recommend daily use of such medications for asthma patients with persistent asthma. Use of such asthma medications can help control and prevent asthma attacks and thus prevent the need for emergency care and hospitalizations. The NHQR also monitors the outcome measure of hospitalizations for asthma. In this case, 1 A third type of measure is less directly related to quality of care. Structural measures reflect aspects of the health care infrastructure that generally are broad in scope, system wide, and difficult to link to short-term quality improvement (for example, a hospital’s staff-to-bed ratio). The NHQR does not use structural measures.

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improvement in the process of prescribing inhaled corticosteroids and proper use by patients is expected to decrease the number of such hospitalizations, as diagrammed below. However, other factors (discussed more fully later in this module) are also important. Effective provider and patient education and self-management are crucial components. Without these, improved outcomes might never occur. Relationship of Process Improvements to Outcomes

Reduced number of hospitalizations for asthma in the total

population

Daily use of medications for

patients with persistent asthma

Selection of Quality Measures for the NHQR The selection of quality measures for the NHQR was based on criteria that include the clinical significance of the measure, reliability of available data, and consensus of the experts. The first NHQR, published in 2003, used a consensus process for determining which measures to include in the national tracking of health care quality. That process included issuing a public call for measures and assembled an interagency task force that reviewed and selected measures according to criteria developed by the Institute of Medicine and adopted by the Interagency Work Group for the NHQR (see box). Other Sources of Asthma Measures The NHQR currently includes only a few asthma measures, but others are available. Some of the major developers of asthma measures are:

• The National Asthma Survey, funded by CDC and tested in 2003, is a 15-minute survey that States can use to provide a comprehensive assessment of asthma in the State. The NAS includes questions found in the BRFSS asthma supplement as well as a more comprehensive set of questions on asthma care. (More information is available at http://www.cdc.gov/nchs/about/major/slaits/nsa.htm).

• The Joint Commission on Accreditation of Healthcare Organizations (JCAHO) Disease-Specific Care Certification Program provides an implementation guide for asthma performance measures for hospitals. A module for children’s hospitalizations for asthma is in development. (More information is available at http://www.jcaho.org.)

• The HRSA Bureau of Primary Health Care supports Health Disparities Collaboratives for disease-specific conditions, including asthma, for primary health care centers to participate in

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learning networks to improve quality of care. These learning collaboratives maintain a registry of asthma patients and monitor care for asthma patients on a monthly basis. (More information is available at http://www.healthdisparities.net.)

• The National Initiative for Children’s Healthcare Quality also develops learning collaboratives for asthma care for children based on the chronic care model for quality improvement.

Criteria for Selecting Asthma Measures: Importance

• Impact on health: What is the impact on the patient? • Meaningfulness: Are providers and patients concerned about this area? • Susceptibility to influence by the health care system: Can the health care system meaningfully address

this aspect or problem? Scientific soundness

• Validity: Does the measure actually measure what it is intended to measure? • Reliability: Does the measure provide stable results across various populations and circumstances? • Explicitness of evidence: Is scientific evidence available to support the measure?

Feasibility and usefulness • Existence of prototypes: Is the measure in use? • Availability of required data across the system: Can information needed for the measure be collected in

the scale and time frame required? • Cost or burden of measurement: How much will it cost to collect the data needed for the measure? • Capacity of data and measure to support subgroup analyses: Can the measure be used to compare

different groups of the population? Source: Adapted from Institute of Medicine, Envisioning the National Health Care Quality Report, 2001.

Multiple Dimensions of Quality for Asthma Care One challenge of initiating quality improvement for asthma care from the perspective of a State or local quality improvement team is selecting from measures that assess the process and outcomes of improved care. There are many measures that could be used to assess different aspects of asthma care. Table 4.1 shows important dimensions of asthma quality of care and the measures that have been developed to assess these dimensions for improving care for asthma. The dimensions include provider processes of care, patient self-care processes, and outcomes of care such as quality-of-life factors. In addition, insurance coverage and prevalence and severity of asthma among the population are important factors that will influence the various measures of asthma care quality in any population. Appendix D lists over 100 measures that are used throughout the country to measure asthma care quality and shows that different organizations evaluate different dimensions of asthma and define measures in different ways. Such variability in measurement makes it difficult, if not impossible, to compare across organizations, settings, and geography. CDC’s National Asthma Survey addresses nearly all of the dimensions of quality asthma care, and the Behavioral Risk Factor Surveillance System surveys address the questions of influenza vaccination and smoking cessation counseling. Only a measure of whether the physician assessed the patient’s asthma severity appropriately is missing. As noted earlier, the NAS was implemented in 2003 and tested

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in a few States. It has been adapted for use as a call-back survey in the BRFSS; the call-back data will be merged with the BRFSS core data so that all the measures in BRFSS will be available for analysis with the asthma-specific data. States can use Table 4.1 as a guide to understand how the measures can be used to assess asthma care quality. Table 4.1. Dimensions of asthma care measurement Category Measure description Importance

Provider Care (Process Measures)

A.1. Asthma severity assessment

Asthma severity is assessed by health professional during a patient visit.

Treatment strategies for asthma involve a stepwise approach in which the level of therapy increases with the asthma severity (see Module 1 for severity classifications). An adequate assessment of severity is thus a key step in determining appropriate management and treatment plans for patients with asthma (JCAHO, 2004). Asthma severity can be assessed by a health professional using a spirometer and taking a history of symptoms. Assessments are important for adjusting appropriate therapy and medication for long-term control of asthma.

A.2. Asthma medications

Use of anti-inflammatory medications (such as inhaled corticosteroids) to control asthma for patients with persistent asthma.

There are two types of medications used for asthma: Anti-inflammatory long-term controller medication and quick-acting relief medication for asthma attacks (bronchodilators). Daily anti-inflammatory medications (or long-term controller medications) can prevent exacerbations and chronic symptoms for patients with persistent asthma. Inhaled corticosteroids are the most effective anti-inflammatory medication available for treating the underlying inflammation of persistent asthma (CDC & NHLBI, 2003). They do not have the serious side effects of oral steroids, especially when properly inhaled. Use of specific asthma medication and frequency of use are measures that show what percentage of asthma patients use medication and how well they understand how to use their medication. However, measures of medication use should be interpreted with knowledge of the severity level.

A.3. Asthma management plans

Patients with asthma who are given a written/documented asthma management plan.

The management goals for controlling asthma can vary for different asthma patients. This is especially important for patients with persistent asthma. Therefore, it is important for providers and patients to discuss goals and how to control asthma. Writing a management plan helps clarify expectations for treatment and provides patients with an easy reference for remembering how to manage their asthma (CDC & NHLBI, 2003).

A.4. Self- management support or patient education

Patients and their families have discussed with their doctors how to manage their asthma and avoid asthma triggers.

Patient education is a key component of asthma care. Because management of asthma generally occurs outside of the doctor’s office after assessment and acute care, it is important for asthma patients and their caregivers to be informed about their asthma. The aim is to help patients manage their asthma in the context of their daily lives. Patients and their families should know how to recognize symptoms, how to avoid triggers, when and how to use asthma medication and delivery devices, and when to seek care. At a minimum, competent asthma education enlists and encourages family support, includes instructions on self-management skills, and is integrated with routine ongoing care (CDC & NHLBI, 2003).

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Planned care visits for asthma are completed at least every 6 months, or more frequently for more severely ill patients or those with comorbidities.

Patients with asthma should seek care at least every 1-6 months depending on asthma severity and ability to control symptoms. Patients with asthma may experience varying symptoms and severity, which may require adjustments in therapy. Because of the nature of asthma, variable exposure to allergens and irritants, or insufficient adherence to a medication regimen, regular followup is recommended (CDC & NHLBI, 2003).

Asthma patients are given influenza vaccines.

A.5. Planned care for asthma

Asthma patients are given smoking cessation counseling.

During planned care visits, persons with asthma may require preventive care for other common conditions since they are more vulnerable to other health complications due to their condition. Flu vaccination is recommended for persons with asthma to prevent asthma exacerbation due to influenza. Smoking is also a trigger for many asthma patients since smoke (first- or second-hand) can exacerbate difficulty breathing.

Patient/Parent Self-Care (Process Measures)

Percent of asthma population that has been advised by a health professional to change things in home, school, or work to reduce asthma triggers.

B.1. Environmental modifications

Percent of asthma population exposed to environmental tobacco smoke.

Environmental and occupational factors contribute to illness and disability from asthma. Decreases in lung function and a worsening of asthma have been associated with exposure to allergens, indoor pollutants (for example, tobacco smoke), and ambient air pollutants (for example, ozone, sulfur dioxide, nitrogen dioxide, acid aerosols, and particulate matter). The patient’s or caregiver’s awareness of environmental triggers is an important part of their ability to manage their asthma and prevent asthma attacks. There are numerous ways to reduce asthma attacks by making changes in the home, school or work such as reducing exposure to dust by removing carpeting or using special linens in the bedroom, removing pets, not smoking, etc. However, the extent to which these changes can be made depends on the patient’s ability to control these environments. Because not all changes are feasible, health providers must understand their patients’ environments and circumstances to give advice.

Outcome Measures

Number of days in the past month with limited activity due to asthma.

Number of school/work days missed in the past month due to asthma.

Number of days with sleeping difficulty in the past month due to asthma.

Number of days with (or free of) asthma symptoms in the past month.

C.1. Daily symptom burden

Frequency of use of beta-agonists for people with asthma.

Asthma attacks and symptoms are indicators of the ineffectiveness of treatment and management of the disease. Also, asthma attacks or symptoms can have a significant impact on a person’s ability to participate in normal daily activities. Sensitivity to environmental triggers can keep a person with asthma from going to work or school. Assessing the number of days with limited activity helps to evaluate the burden of the disease on the population. Also, frequent use of beta-agonists for relief of asthma attacks is an indicator of ineffective long-term control of asthma. By monitoring the frequency of asthma attacks, symptoms, and use of quick-relief medications, access to and effectiveness of treatment can be assessed across the population diagnosed with asthma.

Rate of asthma hospitalizations in the State.

C.2. Acute avoidable events due to asthma (exacerbations) Rate of emergency or urgent care

visits for asthma in the State.

Hospitalization for asthma can often be prevented when the condition is properly managed. Hospitalizations, emergency department visits, or urgent care visits may reflect poor asthma management by patients and their health care providers. Hospitalizations are also highly disruptive to patients and families and increase the cost of asthma care for State Medicaid agencies and State employee benefits programs. Avoidable hospitalization measures are shown in Module 1, Table 1.2.

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Enabling Factor

D.1. Access to care People with asthma who have health insurance coverage in the State.

Health insurance coverage influences the propensity of patients to seek health care in the management of a chronic disease. Without health insurance, families are likely to cut down on routine medications and/or doctor visits for monitoring the condition and to have poorer results in managing it.

Other Factors

Percent of population that has ever been told they have asthma by a doctor or health professional.

Percent of population that currently has asthma.

D.2. Prevalence

Percent of population that has had asthma attack in past 12 months.

Though not modifiable (i.e., primary prevention of asthma is poorly understood), prevalence information provides an indication of the burden of disease on the population and health system.

Data Sources for Asthma Quality of Care Once States have identified the appropriate measures, the next step is locating sources of data for assessing the health system’s performance in delivering quality care for asthma. This section describes three data sources for assessing asthma quality of care: the NHQR, the Behavioral Risk Factor Surveillance System (BRFSS), and local data sources. Asthma-Related Data in the NHQR: Avoidable Hospitalizations The NHQR asthma-related measures are primarily national or regional in geographic scope. At the State level, one asthma measure for outcomes of three age groups (under 18, 18-64, and 65 and over) appears in the NHQR—avoidable hospitalizations related to asthma. As shown in Module 1, Table 1.2 lists that measure by age group, available for 28 States that participated in the Healthcare Cost and Utilization Project (HCUP) in 2004 and whose hospital data are available online through HCUPnet (http://hcupnet.ahrq.gov/). HCUP is a Federal-State-Industry partnership, sponsored by AHRQ, which standardizes data across States. Table 1.2 shows:

• The State’s hospitalization rate adjusted for age and sex differences among the States.

• The difference between the State’s rate and the average of the “best-in-class” States—the 10 percent of States that have the lowest admission rates.

By examining the State rate and the difference from the best-in-class rate, a State can determine how far it has to go to reduce hospitalizations to become a top performer. Hospitalization rates are affected by demographic characteristics of the population such as age, socioeconomic status and race/ethnicity. Although quality improvement efforts do not modify these characteristics, quality improvement initiatives can target subgroups that experience disparities to improve their asthma care quality and improve outcomes such as reducing hospitalizations for asthma.

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The NHQR contains State-level rates only for this outcome measure of avoidable hospital admissions. The NHQR currently excludes State-level asthma process measures because no national consensus has, as yet, established the key asthma measures out of the many that have been developed and used by various organizations. As noted previously, over 100 measures for approximately 50 topics related to asthma care quality are listed in Appendix D. Also, results from the new National Asthma Survey, designed by the CDC to overcome limitations in the BRFSS asthma supplement, were not available in 2003 and 2004 for the first two releases of the NHQR and NHDR. Future releases are expected to include asthma measures from the NAS when available nationwide. Six Asthma Measures in CDC’s Behavioral Risk Factor Surveillance System Currently, the richest source of asthma data nationwide by State is CDC’s Behavioral Risk Factor Surveillance System. However, data from BRFSS should be interpreted with care. Due to sample size limitations, estimates may have large standard errors. The estimates reported here are from the 2004 data year, but several years of data could be pooled together for more reliable estimates. Despite limitations, BRFSS asthma data are a valuable starting point for viewing the national landscape of asthma quality care by State. Table 4.2 summarizes estimates for six measures derived from BRFSS listed in Table 4.1. Each measure is displayed with the three estimates—the national average (reporting States weighted to a national average), the best-in-class average (the 10 percent of States with the best value), and the poorest performing average (the 10 percent of States with the poorest values).

Table 4.2. Six quality measures for asthma: National average, best-in-class average, and poorest performing average, 2004

National Average Best-in-class average

Poorest performing average

Measure Category

(as described in Table 4.1) Measure description

Percent of people

Standard Error

Percent of people

Standard Error

Percent of people

Standard Error

Number of States

reporting

Process Measures

A.2 Medications (in the past month) 69.6 0.9 76.5 2.9 60.6 3.9 25*

A.5 Planned care visits (2 or more in the past 12 months)

30.2 0.9 37.6 3.2 17.8 2.5 25*

Smoking (counseling in the past 12 months)

78.9 1.5 83.9 4.5 64.2 8.2 20 A.5 Flu shots (in the past

12 months) 42.8 0.6 53.0 3.2 33.7 3.3 52**

Outcome Measures

Urgent care visits (in the past 12 months) 28.7 0.9 19.9 2.3 34.6 3.1 25*

C.2 Emergency room visits (in the past 12 months)

17.4 0.7 10.2 1.8 26.7 2.7 25*

* Includes Puerto Rico. ** Includes Puerto Rico and the Virgin Islands Source: Centers for Disease Control and Prevention, Behavioral Risk Factor Surveillance System, 2004.

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Table 4.2 shows that the gap between the best-performing and poorest-performing States varies by type of measure.2 Process measures are practices that clinicians can directly influence. Outcome measures are necessary for State programs and policymakers to assess the effects of changes in processes of care on the outcome of patients with asthma, and thus on the success of the quality improvement program. The spread for all measurements is about 15 to 20 percent. Local Data Sources Finding appropriate data can be a challenge for quality improvement programs. To stimulate interest and start the quality improvement process, States can develop an inventory of local data sources. (See Appendix F for a summary of asthma-related data sources.) Local data (whether by State, county, municipality, or individual health care provider) are essential for quality improvement programs to have a local impact. Local leaders and health care professionals must see their own data compared with those from other providers and with State, regional, and national benchmarks in order to appreciate the importance of their work. By developing a complete inventory of data systems available at the State and local level, States can avoid duplicate data collection and reduce data-related costs. Also, a review of local data in the context of national data should clarify where existing local surveys or data systems could be improved. Some possible local data sources to consider are listed below. These data sources may or may not be health care specific, but they may afford important opportunities for collaborations with various State or local agencies. It may be possible to add questions to ongoing local surveys to inform quality improvement activities for asthma. Children and Youth:

• State school health surveys, administered before entering public schools to assess youth health risk behavior, may include questions about asthma prevalence, activity limitations due to asthma, etc.

• The Youth Tobacco Survey, administered by State health departments, may include questions on asthma prevalence to assess health risks and health behavior related to tobacco use.

• The Youth Risk Behavior Surveillance System, administered by CDC and State and local health and education agencies, monitors health-risk behaviors that contribute to unintentional injuries and violence, tobacco use, alcohol and other drug use, unintended pregnancy and sexually transmitted diseases, unhealthy dietary behaviors, and physical inactivity.

Adults: • Occupational health surveys may provide data on work environment and triggers for

asthma, activity limitation, and number of work days missed due to asthma.

2 As shown in Table 4.2, the number of States (including DC and U.S. territories) reporting on each measure varies. For more detail on BRFSS estimates and individual State estimates of BRFSS measures, see Appendix E.

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Community/Environmental Assessment: • Community surveys may provide local data on environmental factors that affect asthma

and may compare asthma prevalence and outcomes by county, city, or neighborhood levels.

Most States also have ongoing surveys or health data systems that collect data at the State, county, and sometimes provider level. Some of those data systems include:

• State-level BRFSS data, available through the State health department.

• Statewide inpatient hospital discharge systems that collect data on individual discharges from hospitals and can provide county-level and, sometimes, hospital-level data. National benchmarks are available for these types of data through the NHQR.

• State vital statistics include mortality rates by cause of death and race/ethnicity. The National Vital Statistics System, which compiles these State data, can provide uniform national estimates and State estimates.

• Special disease registries focused on asthma may exist within the State, and these provide a rich source of patient-level information on severity and adherence to tracked treatments.

• Other special data collection of State departments of health statistics and other State programs may be modified to address asthma.

Specific data systems for populations that the State supports are also available in most States. These include:

• Medicaid information systems based on health care provider claims for Medicaid reimbursement.

• State employee health benefit claims for reimbursement.

• Patient records from State- or county-operated programs, such as mental health and substance abuse programs, public assistance, or justice systems.

Examples of State-level data sources are available at the National Association of Health Data Organizations Web site at: http://www.nahdo.org/soa/soalist1.asp?Category=State%20Agency. Other Federal or national asthma surveillance systems compile data that describe State and local populations or health resources. These include:

• NHLBI Web site, a valuable starting place to identify data and become familiar with the network of organizations and individuals associated with asthma data collection on the State and national level (http://www.nhlbi.nih.gov).

• Census population data by State, maintained by the U.S. Bureau of the Census. These data are helpful for describing the demographic characteristics and wealth of local areas (http://eire.census.gov/popest/data/states.php).

• The Area Resource File, a county-level database of health care resources from several surveys and data sources, compiled by the Health Resources and Services Administration. This resource might be helpful in analyzing the health resources available

on a county level.

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• Quality of care in managed care organizations, provided through the National Committee for Quality Assurance (see: http://www.ncqa.org/index.asp). Local managed care organizations can be an important source of local data on health care quality.

• The Henry J. Kaiser Family Foundation Web site (http://kff.org/statepolicy/index.cfm), a rich source of health and other information at the State-level compiled from many public databases and published studies. This may help identify differences among State environments that would explain asthma prevalence or treatment differences across States.

• American Lung Association Web site, which contains patient education materials and tools as well as research on asthma (http://www.lungusa.org).

Using Benchmarks To Develop State Performance Estimates

Once States have identified measures and acquired relevant data, analysts must develop estimates that gauge State performance. Benchmarks Benchmarks are external markers or values against which States can measure performance. The benchmark can represent the national average or best performers. How the State fares depends on where the State estimate falls compared with the benchmark. The NHQR provides a national set of estimates and State estimates that can be used as benchmarks for quality improvement. Several types of metrics or benchmarks can be used for assessing a State. From more to less stringent, they include:

• The theoretic limit of 100 percent achievement (or 0 percent occurrence for avoidable events), which is an ideal, but often impractical or even impossible goal.

• A best-in-class estimate of the top State or top tier of States that shows what has been achieved (e.g., the top 10 percent of States is used in this Resource Guide).

• A national consensus-based goal, such as Healthy People 2010, set by a consensus of experts; such goals may be set more or less stringently than other benchmarks.

• A national average over all States, which shows the norm of practice nationwide but, being an average estimate, will represent a weaker goal than the best-in-class estimate.

• A regional average, which a State can use to compare itself to other States that are more likely to face similar environments; but, as a goal, it may be less aggressive than the best-in-class goal.

• An individual State rate, which itself can be used as a baseline against which to evaluate State-level interventions and progress over time within the State or to offer as a norm for local provider comparisons.

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Some of these benchmarks can be found in the NHQR—the national and regional averages. The best-in-class estimate, not reported in the NHQR, can be derived from data in it. See Appendix G for details on the best-in-class estimate and other benchmarks that can be derived from the NHQR. Appendix H describes how to conduct statistical significance testing to determine whether or not comparisons of estimates show significant differences. Asthma Benchmarks for States A focused and limited set of measures for tracking quality nationally on an ongoing basis has not yet been specified for asthma. Thus, the NHQR has not yet settled on a complete set of consensus-based measures for asthma. As noted above, the National Asthma Survey is expected to inform that process in the future. For this Resource Guide, benchmarks were calculated for asthma measures that were chosen based on availability of BRFSS data at the State level, current clinical guidelines, advice from an expert steering committee, and measures that will have a direct link to State budgets. They include:

• Process measures—services important for controlling asthma and preventing complications: o Routine checkups for patients with asthma (two or more planned doctor visits in the past

12 months). o Medications (use of medication to control asthma). o Advice to quit smoking (for asthma patients who smoke). o Flu shots (recommended for patients with asthma).

• Outcome measures—avoidable health care use: o Urgent care visits for asthma. o Emergency room visits for asthma.

Table 4.2 includes benchmarks—the national and best-in-class averages—for these measures. Figure 4.1 shows regional variations and the extent of the spread between States for each measure. The State analyses which follow illustrate four of these measures in more detail. BRFSS has limitations for establishing benchmarks for State performance, including limited questions on asthma and other technical issues. These are discussed further in Appendix E.

Figure 4.1. Six quality measures for asthma: national average, best-in-class average, and State variation, by region, 2004

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Source: Calculated from BRFSS data, 2004. For State estimates for adults by age group, see Appendix E as follows: urgent care visit, Table E.5; emergency room visit, Table E.6; routine care, Table E.10; medications, Table E.12; smoking cessation counseling, Table E.15; flu shot, Table E.16.

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Studying Individual States Against Benchmarks This section compares four States, as examples, to the key benchmarks for the asthma measures. The four States were chosen because they show variation across the measures in how States ranked against the benchmarks in 2004. In Figures 4.2-4.5, States are compared to a national average and a best-in-class State average for each measure. Though the theoretic limit may be difficult to achieve for many valid reasons, some States have already achieved the best-in-class estimate. Although the average over all States is often used to assess a State’s performance, aiming for it means the State aims to be average rather than the best. Also, in some cases, a quality improvement team may set goals higher than the best-performing States because they may view all States as poor performers. Figure 4.2. Percent of adults with asthma with routine checkups, medications, urgent care visits, and emergency room visits, 2004: Maryland compared to benchmarks

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Source: Calculated from BRFSS 2004 data. Maryland. Figure 4.2 reveals the following about asthma care in Maryland compared with national benchmarks in 2004 and based on statistical tests. The marked “goal” on the vertical axis indicates the direction of improvement rather than an achievable value.

• In 2004, Maryland was close to the national average benchmark on two measures of asthma care quality—routine checkups and urgent care visits. Maryland has room for improvement on these dimensions to become a best-performing State.

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• Maryland was below the national average for percentage of asthma patients who take medications for asthma. Given the importance of medication use to control asthma symptoms and prevent asthma attacks and their position on this measure, Maryland may want to investigate asthma drug therapy within the State, determine the locales or subpopulations for which such therapy is lacking, and develop a targeted program to improve the use of prescription drugs in the State for residents with asthma.

• Maryland appears to be statistically no different from the national average and the best-in-class average for emergency room visits. Small samples interfere with the ability of the data to distinguish between average and best-in-class in this case. Because of the weakness of this test and because the percent of people with emergency visits is higher in Maryland than nationally, Maryland might want to determine this rate more precisely. Maryland statewide hospital emergency department data system may want to address this issue.

Maryland can improve the treatment of asthma in the community and reduce the number of expensive emergency services in the State. Also Maryland has an opportunity to reduce its hospitalizations of patients with asthma for all age groups (see Table 1.2). Figure 4.3. Percent of adults with asthma with routine checkups, medications, urgent care visits, and emergency room visits, 2004: Michigan compared to benchmarks

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Michigan. Figure 4.3 reveals that: • In 2004, Michigan was at the national average benchmark for two measures—routine

checkups and urgent care visits. Thus, Michigan has room for improvement on these dimensions to become a best-performing State.

• Michigan appears to be statistically no different from the national and best-in-class averages for the other two measures—use of asthma medications and emergency room visits. Small samples impede a robust test between average and best-in-class for these important measures, which points out the need for better assessment methods. Larger samples for BRFSS may be a relatively inexpensive solution for better statistics.

• Michigan was not among the below-average States for any of these four asthma measures in 2004. This suggests that Michigan’s efforts toward disease prevention and control may have contributed to this positive result. Michigan also is helped by its average sociodemographic characteristics, especially an average poverty rate.

Thus, Michigan may want to improve its strategy for measuring asthma care quality and could justify focusing attention on improving the frequency of checkups for people with asthma in order to become a best-performing State. If that strategy is done well, it could reduce the cost of expensive urgent/emergency care. Also, by improving outpatient care Michigan has the opportunity to in turn reduce costly hospitalizations related to asthma (see Table 1.2). Figure 4.4. Percent of adults with asthma with routine checkups, medications, urgent care visits, and emergency room visits, 2004: New Jersey compared to benchmarks

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New Jersey. Figure 4.4 shows that:

• In 2003, New Jersey was among the best-in-class States for routine checkups for people with asthma. Although this is excellent performance among all States, the best performers only reach the 38-percent mark for the percent of people with asthma who have planned care visits two or more times per year. Thus, New Jersey may want to aim for higher checkup rates for its population with asthma.

• New Jersey’s estimate for asthma medication use was statistically no different from the national average or best-in-class average. Again, the small samples blur the ability to make the distinction, but the value of the estimate is closer to the all-States average than the best-in-class States estimate. Another factor with this measure is that the spread of the values across the States is very narrow, suggesting that medication use in asthma care is relatively uniform across the States.

• New Jersey was worse than or at the national average on the use of urgent and emergency care, respectively.

Despite New Jersey’s excellent performance on checkups and reasonable performance on medication therapy, its poorer performance on use of expensive urgent care services raises a question. How effective are community-based checkups for people with asthma if they use a high level of urgent care services? New Jersey may want to explore the nature of checkups for people with asthma and determine whether health care providers are using the best asthma management practices (see Module 1) with their patients. Figure 4.5. Percent of adults with asthma with routine checkups, medications, urgent care visits, and emergency room visits, 2004: Vermont compared to benchmarks

 

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Vermont. Figure 4.5 shows that:

• In 2004, Vermont was among the poorest-performing-States for routine checkups for people with asthma.

• Vermont appears to be statistically no different than the national average and best-in-class average for use of asthma medications. Again this distinction cannot be made definitely due to small sample size.

• Vermont was among the best-in-class States for the two outcome measures related to expensive emergency medical care. Vermont has low rates of urgent care visits and emergency department visits for asthma.

This result—reasonable outcomes for emergency services use, but poor processes for checkups and medication, which appear to be inconsistent with each other—suggests that these measures by themselves are not the strongest determinants of patient outcomes and that other underlying factors are at work. The next section discusses some of the external factors that can affect the quality of care in communities.

Factors That Affect Quality of Asthma Care The State data presented above raise several questions for anyone involved in quality improvement. What does a State’s position on the continuum of quality measures mean? What factors influence that position and the variability among the States? What factors can be influenced through State policy change and local efforts? A number of factors influence quality and outcomes of health care for any disease, as Figure 4.6 shows. Some factors may be difficult to change, such as biologically inherited traits; income, education, and social status; and general population characteristics. Others may be changeable in the medium or long term, but not in the short term, such as the supply of health care professionals, the makeup and mission of health care organizations, and the disease prevalence of the population (which represents ingrained patterns of personal behaviors and health system effectiveness). All of these factors influence the process and outcome of health care. Although State government and community leaders do not have control over all factors, State actions can influence some important factors to promote positive change. These include educating people with asthma about the risks of uncontrolled asthma, raising awareness among professionals about health care processes that can improve outcomes for people with asthma, raising awareness in schools and communities about mitigating risk factors that can trigger asthma attacks, and creating financial incentives to encourage providers to manage diseases with their patients. Some States, for example, target programs to affect patient self-management and other external causes toward minority populations that are disproportionately affected by asthma. To better understand what influences a State’s position and how it compares with other States, some of the factors presented in Figure 4.6 are discussed in more detail below.

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Figure 4.6. Factors that affect disease process and outcome measures

Patient Risk of Disease:•Biological•Socioeconomic

Quality of Care:•Process Measures•Outcome Measures

Provider Ability to Manage Disease:•Incentives•Knowledge•Educational tools•Referral networks

External Environment:•Disease prevalence•Health care system

Difficult-to-Change Factors Feasible-to-Change Factors

Patient Ability to Self-Manage:•Behavior•Knowledge•Compliance

External Environment:•Access to care•Health care resources•Government resources•Government leadership•QI programs

Measures

Patient Risk of Disease:•Biological•Socioeconomic

Quality of Care:•Process Measures•Outcome Measures

Provider Ability to Manage Disease:•Incentives•Knowledge•Educational tools•Referral networks

External Environment:•Disease prevalence•Health care system

Difficult-to-Change Factors Feasible-to-Change Factors

Patient Ability to Self-Manage:•Behavior•Knowledge•Compliance

External Environment:•Access to care•Health care resources•Government resources•Government leadership•QI programs

Measures

Patient Risk of Disease:•Biological•Socioeconomic

Quality of Care:•Process Measures•Outcome Measures

Provider Ability to Manage Disease:•Incentives•Knowledge•Educational tools•Referral networks

External Environment:•Disease prevalence•Health care system

Difficult-to-Change Factors Feasible-to-Change Factors

Patient Ability to Self-Manage:•Behavior•Knowledge•Compliance

External Environment:•Access to care•Health care resources•Government resources•Government leadership•QI programs

Measures

Racial, Ethnic, and Socioeconomic Factors The socioeconomic makeup of a State will likely play a role in how it compares to national norms on process and outcome measures. States with a higher proportion of individuals living in poverty, lower average education, and a more diverse racial and ethnic population, for instance, will likely find poorer outcomes for their population compared to the national population (IOM, 2003). The NHDR (AHRQ, 2003; 2004a) summarized the racial, ethnic, and socioeconomic differences in asthma across the Nation (but not by State). Nationwide, minority or lower socioeconomic status is associated with higher asthma prevalence, higher asthma death rates, higher rates of serious asthma complications, and poorer asthma outcomes. (Blacks, for example, are much more likely than Whites to be hospitalized for asthma; see Table 4.3). The socioeconomic makeup of a State, therefore, should play a role in the strategies that the State uses to improve asthma care quality. For instance, States that target efforts to improve asthma care at population groups particularly at risk for asthma complications should also be able to improve their overall performance on asthma care quality.

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Table 4.3. Asthma hospitalizations by race/ethnicity and community income, United States, 2001

Race/ethnicity & Income1 EstimateStandard error P-value Estimate

Standard error P-value Estimate

Standard error P-value

All IncomeTotal 197.213 13.937 113.221 3.384 164.407 5.254

White Non Hispanic 138.531 8.417 86.794 2.234 134.421 4.770Black Non Hispanic 450.500 52.994 0.000 289.492 22.208 0.000 350.680 30.096 0.000

Hispanic 187.549 23.640 0.051 131.084 14.294 0.002 269.780 31.601 0.000Asian and Pacific Islander 82.070 11.078 0.000 81.168 11.327 0.626 265.633 41.565 0.002

Other 479.712 54.078 0.000 287.163 29.021 0.000 570.597 61.084 0.000Less than $25,000Total 320.780 61.432 247.247 34.825 259.808 32.743

White Non Hispanic 168.288 18.127 140.991 11.653 155.496 18.086Black Non Hispanic 491.382 96.659 0.001 471.882 86.934 0.000 471.283 90.216 0.001

Hispanic 295.895 115.834 0.276 213.124 70.749 0.314 360.829 94.130 0.032Asian and Pacific Islander 32.194 14.549 0.000 70.704 26.821 0.016 169.517 91.263 0.88

Other 222.329 54.795 0.349 240.679 62.787 0.119 369.948 114.972 0.065$25,000-$34,999Total 263.164 21.849 149.112 6.753 194.038 9.592

White Non Hispanic 180.069 11.659 115.857 4.911 161.635 9.042Black Non Hispanic 535.166 72.011 0.000 318.593 28.612 0.000 350.476 35.187 0.000

Hispanic 222.134 33.903 0.241 156.160 23.101 0.088 336.245 60.625 0.004Asian and Pacific Islander 72.055 12.197 0.000 65.869 12.952 0.000 206.572 50.218 0.378

Other 353.319 58.711 0.004 219.008 30.510 0.001 428.836 81.411 0.001$35,000-$44,999Total 190.585 15.653 110.463 4.773 155.947 7.552

White Non Hispanic 138.866 10.679 87.823 3.895 128.315 6.663Black Non Hispanic 433.132 61.370 0.000 263.376 23.811 0.000 331.170 36.243 0.000

Hispanic 157.770 21.546 0.432 116.240 14.537 0.059 253.628 34.592 0.000Asian and Pacific Islander 59.070 11.869 0.000 81.666 18.631 0.746 290.330 65.565 0.014

Other 529.759 71.266 0.000 298.926 50.589 0.000 693.164 134.304 0.000$45,000 or moreTotal 152.433 13.900 85.672 3.920 158.891 8.595

White Non Hispanic 120.982 10.700 72.609 3.354 136.513 7.994Black Non Hispanic 359.443 45.173 0.000 208.308 20.511 0.000 330.666 43.173 0.000

Hispanic 158.914 25.643 0.172 107.003 11.603 0.004 248.074 31.343 0.001Asian and Pacific Islander 92.105 15.234 0.121 84.922 14.822 0.418 321.229 63.161 0.004

Other 818.261 128.185 0.000 370.707 40.786 0.000 869.244 119.059 0.000

Source: Agency for Healthcare Research and Quality, Healthcare Cost and Utilization Project, State Inpatient Databases, disparities analysis file, 2001. This file is designed to provide national estimates on disparities using weighted records from a sample of hospitals from the following 22 states: AZ, CA, CO, CT, FL, GA, HI, KS, MD, MA, MI, MO, NJ, NY, PA, RI, SC, TN, TX, VA, VT, and WI.

1 Median household income is based on ZIP Code data obtained from Claritas linked to patient ZIP Code in the HCUP database.

Pediatric admissions per 100,000 population less than 18 years

PQI 4

Adult admissions per 100,000 population

age 18 and overPQI 15

Adult admissions per 100,000 population

age 65 and overPQI 15

Denominators were obtained from ZIP-Code-level population counts by age, gender, race, and ethnicity based on U.S. censuses for 2000 and 1990 and Claritas methods to deal with race/ethnicity coding changes between those years.

P values to test race category compared to white category.

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Biological and Behavioral Factors Understanding biological and behavioral influences on asthma should help in developing assessment tools and interventions for preventing or reducing the burden of asthma. Risk factors for asthma include (King et al., 2004):

• Parental history of asthma. • Early-life stressors and infections. • Obesity. • Exposure to indoor allergens, tobacco smoke, and outdoor pollutants. • Work-related exposures.

Socioeconomic factors may be related to underlying biological factors or behavioral factors. The accumulated stress of poverty, low levels of control in jobs and relationships, low job and life satisfaction, and societal discrimination against minority groups can influence health status (Williams, 1999). Physical Environment The physical environment in which asthma patients live is an important contributor to their asthma severity. The presence of poor air quality, dust, pets, cockroaches, and other allergens can affect how well a patient is able to control his or her asthma. A recent study released by the National Institutes of Health shows the connection between decayed bacteria in bedrooms and other rooms of a house and asthma prevalence (Thorne et al., 2005). External Environment In addition to individual characteristics (some of which are amenable to change with personal motivation), each State has a different infrastructure and different environmental factors over which policymakers may or may not have control. These factors include the collective health status of the population, the distribution of health care services within locales, the distribution of wealth and tax resources among communities, and government programs and leadership. State leaders will face different health care system challenges, including:

• Health system infrastructure—Availability of health professionals, emergency rooms, and hospitals beds.

• Uninsured populations—Presence of vulnerable and uninsured populations and the need for special State programs to cover the cost of health care for them.

• Safety net infrastructure—Availability of a safety net of health care providers as a last resort for those who cannot afford health insurance and private health care.

• Provider knowledge—Providers who have sufficient state-of-the-art knowledge to manage asthma effectively and to educate their patients in asthma self-management.

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• Public education—Public education programs that raise patient awareness of the warning signs of the disease, its potential complications, the importance of diet and exercise, and the effectiveness of personal self-management, including knowing when to consult a doctor.

• Government resources—Funds, in a time of tight State budgets, to stimulate quality improvement activities related to asthma care.

• Leaders to champion quality improvement—Leaders who can draw attention to the problems associated with asthma and harness the commitment of health professionals to change practices and monitor results.

• Knowledge of what to do—Identification of effective quality improvement programs that are based on scientific evidence.

• Adequate data systems to assess progress—Availability of data systems that can provide comparable measures across providers, communities, and States.

The inter-relationship among all of the factors in Figure 4.6, then, affects how a State compares with other States on measures of asthma care quality. It is difficult to measure all of these factors at the State level. An attempt was made to analyze the BRFSS measures in Table 4.2 against individual State-level environmental factors—prevalence of asthma, emphysema and chronic bronchitis in the population, the percent of the population below poverty level, racial/ethnic makeup of the population, the HMO penetration rate, the supply of hospital beds, and air quality in the State. The findings were not consistent enough across measures and factors to be believable. Again, the small sample sizes and imprecision of the asthma estimates themselves may be the limiting factor. Moreover, survey averages (e.g., percent having planned care visits) related to State aggregates from other sources (e.g., percent of the population that is uninsured) do not provide a direct test of these relationships. With large databases, it is possible to assess asthma care quality at not only the State but also local levels for some measures. For example, HCUP data and the statewide discharge data systems that are the source of HCUP data (with its hundreds of thousands of discharge records per State per year) support analyses at the county or other market areas. County-level data related to health care resources are generally available, although county data on health risk behaviors of the population generally are not. State analysts could use their county-level databases to compare asthma outcome measures based on HCUP data—e.g., asthma hospitalizations—or on data from their statewide data organization with other county characteristics. AHRQ’s Prevention Quality Indicator software can be applied to a State’s discharge data to produce county-level statistics.

Summary and Synthesis

Local leaders and health care professionals must see their own data in comparison with other provider data and with State, regional, and national benchmarks in order to appreciate the importance of their work. Assessing State quality of care for asthma begins with identifying quality measures. These fall into two main groups: process measures, which reflect the quality of care delivered, and outcome measures, which reflect patient health status. The former are needed to guide health care providers on how to change, the latter are needed to know whether

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the changed processes have had the intended effect. Data (whether State, county, municipal, or individual health care provider data) are essential for quality improvement programs to have an impact locally. Ideally, improvements in particular processes yield improvements in the associated outcomes. The NHQR provides a starting point for accessing consensus-based measures. The NHQR provides estimates for asthma hospitalizations by State. In addition, BRFSS estimates are used to assess asthma care quality by State. Although consensus on a few key measures of asthma care quality has not yet evolved, this Resource Guide provides an inventory of some measures. Data are essential to improve quality. States need performance data on asthma care to gauge their own performance against national benchmarks and to focus quality improvement efforts by identifying potential problem areas. This Resource Guide provides a list of national, State, and local sources for estimates for asthma, asthma care, and other related information. This module also shows how data can be analyzed and interpreted to answer the global question: How does my State compare with other States and national benchmarks on health care quality for asthma? State-level baseline estimates across all conditions studied in the NHQR afford State leaders a broad view of health care quality in their State. More refined questions about areas within the State will require local data and analysis. Resources for Further Reading Data and Data Tools on the Internet Many data resources are available on the Internet, including many sources used in the NHQR and NHDR. Some Web sites allow users to manipulate the data to produce tables and other useful outputs. Such resources include:

• HCUPnet

http://hcupnet.ahrq.gov/ HCUPnet allows users to select national statistics, or detailed statistics for certain States, for various conditions and procedures. The interactive program also allows users to compare types of patients and types of hospitals. These statistics are based on data received from Statewide hospital discharge data programs for inclusion in HCUP.

• HCUP User Support (HCUP-US) http://www.hcup-us.ahrq.gov/home.jsp This Web site is designed to answer HCUP-related questions; provide detailed information on HCUP databases, tools, and products; and offer assistance to HCUP users.

• AHRQ Quality Indicators

http://www.qualityindicators.ahrq.gov/ The AHRQ Quality Indicators are measures of health care quality that make use of readily available hospital inpatient administrative data. Asthma measures can be found in the Prevention Quality Indicators module.

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• MEPSnet http://www.meps.ahrq.gov/mepsweb/data_stats/meps_query.jsp This Web site offers users statistics and trends about health care expenditures, utilization, and health insurance, including national and regional health insurance estimates.

• BRFSS – Annual Survey

http://www.cdc.gov/brfss/technical_infodata/index.htm This Web site has detailed technical information about the survey in addition to downloadable data sets in ASCII and SAS formats.

• BRFSS

http://www.cdc.gov/brfss/ This Web site provides useful background information about the BRFSS implementation, technical information, and documentation.

• CDC Faststats – Asthma

http://www.cdc.gov/nchs/fastats/asthma.htm The Faststats Web site provides easy access to statistics on topics of public health

importance. The asthma page has a general overview of asthma statistics and links to specific data sources for more information on national data for asthma.

Asthma Registries Some additional Web sites offer links to useful tools and information to facilitate data collection at the local level. Two Web sites that offer instruction for implementing asthma registries to track the treatments given to people with asthma are: • http://www.healthdisparities.net/hdc/html/collaborativesOverview.aspx

This Web site, associated with the HRSA Health Disparities Collaboratives, offers a number of useful tools, including helpful information for creating and assessing computer registries.

• http://www.improvingchroniccare.org/improvement/docs/ICIC_Registry_Comparison_Octob

er02.xls This Web site offers a comparison of asthma registries.

Other Useful Web Sites Agency for Healthcare Research and Quality — http://www.ahrq.gov/

National Asthma Control Program —http://www.cdc.gov/asthma/NACP.htm

National Committee for Quality Assurance — http://www.ncqa.org/

National Quality Forum — http://www.qualityforum.org/

National Guideline Clearinghouse — http://www.guidelines.gov/

National Asthma Education and Prevention Program— http://www.nhlbi.nih.gov/about/naepp/

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Associated Appendixes for Use With This Module Appendix D: Asthma Measures Appendix D is an inventory of available national (Table D.1) and local (Table D.2) asthma measures and sources. Measures are categorized by topics related to asthma care. Appendix E: BRFSS Measures, Data, and Benchmarks Appendix E provides the results of significance tests for 2003 BRFSS State estimates compared to the national average of each measure and compared to the best-in-class estimates for each measure. P-values less than 0.05 are considered statistically significant. State estimates that have p-value less than 0.05 are statistically different from the comparison estimate (national average or best-in-class). State estimates that have p-value greater than 0.05 are not statistically different from the comparison estimate. Appendix F: Other Data Sources Appendix F summarizes data sources used in this Resource Guide other than BRFSS data. This appendix includes descriptions and tables, where available, of national data sources (HCUP, HEDIS®, MEPS, and NHDS) and local data sources available from some States. Appendix G: Benchmarks From the NHQR Appendix G provides additional detail on benchmarks that can be derived from the NHQR and explains how they were developed and defined for this Resource Guide. This appendix discusses the best benchmarks for stimulating quality improvement, emphasizing that methods used to generate the benchmarks must be understood to ensure they are compatible with a State’s estimates. Appendix H: Information on Statistical Significance Appendix H shows how to compare State estimates to benchmarks using statistical significance and p-values that take into account the expected random variation in estimates. This appendix also shows how to calculate p-values when estimates and standard errors are provided.

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Module 5: Moving Ahead – Implications for State Action This module draws implications for States to move forward with their own asthma care quality improvement initiatives. States need to go beyond generic resources and tailor efforts to their individual needs; specifically, they need to gather more localized data and involve key partners.

Asthma is a chronic condition that presents a compelling case for quality improvement: • Asthma is becoming more prevalent. Estimates show that the number of Americans with

asthma nearly doubled between 1980 and 1996. In 2007, nearly 30 million Americans reported suffering from asthma at some time in their lives.

• Substantial disparities exist in diagnoses and quality of asthma care. Data from the NHQR and NHDR show wide variations in quality for asthma across States and also across different socioeconomic, racial, and ethnic groups.

• Asthma cannot be cured but is highly treatable. It can be controlled and managed through a range of interventions and treatments that prevent attacks and allow people with asthma to function normally. Often the disease can be self managed, allowing people with asthma to avoid costly hospitalizations or procedures.

• Asthma is costly to treat. Families with an asthmatic child reported spending nearly three times as much annually on health care as families with a non-asthmatic child. In 2007, asthma’s total estimated cost for the total burden of the illness (including health care services and other lost abilities) was about $19.7 billion.

Thus, there is potential for a substantial return on investment for purchasers and the health care system as a whole through asthma quality improvement. Essential Elements in State-Led Quality Improvement This Resource Guide offers a model for States to undertake a systematic effort to improve the quality of asthma care within their jurisdictions. State leaders can contribute essential elements to the process of asthma care quality improvement. These elements include the following • Providing leadership and vision. Quality improvement requires leadership. Whether

initiatives are developing locally, statewide or nationally, effective leadership is essential to quality improvement. It will not emerge without a champion who can provide leadership to help organizations and individuals develop a shared vision and common goals for health care quality. Leadership must be a catalyst for others to become involved in developing shared vision and goals for improving health care quality.

• Forming partnerships and collaborations. In addition to leadership and vision,

partnerships and collaborations are vital to improving quality. Health care quality is the product of many different parts of the health care system but ultimately must affect what happens in the community, the patient environment, and the clinical setting. Thus, all groups that affect patient care should participate in quality improvement efforts. Health care

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professionals and providers need to establish systems that deliver appropriate, quality care consistently; patients need to demand and participate in the best available care; and purchasers must demand and pay for the highest quality, most cost-effective delivery of care. Consumer groups with interest in asthma can be powerful allies for change and a source of expertise. State health department staff and other asthma care experts from private-sector organizations can provide support and expertise for State initiatives.

• Initiating measurement and reporting. A key step for State action is measuring quality.

This step involves defining quality standards, identifying measures of those standards, finding available benchmarks, locating data that pertain to the State, perhaps collecting new data, and using data to track how well the health care system is performing and how well pilot interventions are working. Benchmarks and comparison data provide a mechanism for assessing how well the State is doing and how well any given health plan or provider is doing in a selected area of care.

In order to improve, the health care system must have data robust enough to estimate a given set of measures of health care quality. As part of a systematic improvement initiative, States will need to go beyond generic resources and develop a comprehensive, State-specific picture of asthma care. Doing so will enable States to identify specific quality problems in their own communities and tailor specific solutions. Results then must be made available to enable purchasers and consumers to make meaningful decisions based on the performance of various providers.

• Assisting planning and goal setting. State quality improvement initiatives should involve

development of an action plan with specific goals for quality improvement in the State. The action plan must include timelines for specific steps and deliverables to help ensure that all partners move together. The plan should include specific responsibilities and benefits for as many project partners as possible to ensure commitment and continued involvement.

• Assuring evaluation and accountability. After establishing partnerships with committed

leaders and a common vision and goals, developing measures, collecting and analyzing data, and setting goals, there also is a need for evaluation of both health system performance and accountability for health care quality. Evaluation allows partners to identify the most troublesome areas and devote resources and attention to those areas where improvement is needed. Evaluation also enables recognition of areas where there is solid performance or conduct improvement over time. It may require some technical input and expertise, but it is an important component of the quality improvement process. Without evaluation, impact of the program will be unknown and future direction for the program will be haphazard.

• Creating incentives. Although reporting data on performance is often enough to spur low

performers toward improvement, tying rewards to high performance is also needed. The American health care system typically pays providers for the level, not the quality, of services delivered. However, States—as large health care purchasers—are in a position to offer financial incentives for providers to deliver quality care. For example, State programs can include bonuses and rewards for physicians who follow evidence-based guidelines in delivering asthma care.

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• Spreading the change statewide. It is important for States to develop and implement ways

to spread quality improvement in asthma statewide. This involves planning an effective tracking system, collecting and analyzing data, and drawing conclusions. State leaders must differentiate between sound conclusions and inconclusive findings and use this information to further the asthma quality improvement effort and to address other health care issues.

States Have a Way Forward—A Final Note This Resource Guide has attempted to demonstrate for State leaders the need for quality improvement in asthma. Much has been done, but data from the NHQR show that much remains to be done to achieve quality care for all people with asthma. By reviewing and analyzing the information in this Resource Guide, assessing the local context, and designing an asthma quality improvement strategy, State leaders can identify opportunities to make a difference in the quality of care their constituents receive. The experiences of States that have implemented quality improvement for asthma care provide valuable insights into what can be accomplished through innovative, visionary efforts by State leaders.

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Appendix A: Acronyms Used in This Resource Guide AHRQ = Agency for Healthcare Research and Quality

ALA = American Lung Association

AMA-PCPI = American Medical Association Physicians Consortium for Performance Improvement

BRFSS = Behavioral Risk Factor Surveillance System

CASI = Chicago Asthma Surveillance Initiative

CDC = Centers for Disease Control and Prevention

CMS = Centers for Medicare and Medicaid Services

ED = Emergency department

EPA = Environmental Protection Agency

HHS = U.S. Department of Health and Human Services

HCUP = Healthcare Cost and Utilization Project

HEDIS® = Health Plan Employer Data and Information Set

HRSA = Health Resources and Services Administration

IOM = Institute of Medicine

JCAHO = Joint Commission on Accreditation of Healthcare Organizations

JCAHO-APMIG = Joint Commission on Accreditation of Healthcare Organizations, Asthma Performance Measurement Implementation Guide MEPS = Medical Expenditure Panel Survey

MQIC = Michigan Quality Improvement Initiative Guideline

NACP = National Asthma Control Program

NAEPP = National Asthma Education and Prevention Program

NAS = National Asthma Survey

NICHQ = National Initiative for Children’s Healthcare Quality

NCHS = National Center for Health Statistics

NCQA = National Committee for Quality Assurance

NHDR = National Healthcare Disparities Report

NHIS = National Health Interview Survey

NHLBI = National Heart, Lung, and Blood Institute

NHQR = National Healthcare Quality Report

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NIH = National Institutes of Health

NQF = National Quality Forum

NYCCAI = New York City Childhood Asthma Initiative

PDSA = Plan-Do-Study-Act

SAMHSA = Substance Abuse and Mental Health Services Administration

SCHIP = State Children’s Health Insurance Program

VHOP = Virginia Health Outcomes Partnership

YRBSS = Youth Risk Behavior Surveillance System

Appendix B: Medicaid Spending on Asthma by State Table B.1. Medicaid: White eligibles by State and age group and estimated spending, 2004

StateWhite Medicaid eligibles 20041

White Medicaid eligibles 0-18 with

asthma1,2,4

White Medicaid eligibles 19-64 with

asthma1,2,4

White Medicaid eligibles 65+ with

asthma1,2,4

Estimated Medicaid spending on asthma for White eligibles3

Total U.S. 24,963,746 979,057 630,435 162,590 $1,966,145,264

Alabama 428,801 16,162 10,540 3,611 33,631,762Alaska 54,040 2,660 1,050 203 4,341,122Arizona 485,844 17,696 14,784 1,903 38,148,461Arkansas 438,290 18,702 10,123 2,499 34,753,634California 2,249,008 71,172 74,412 12,042 174,888,333Colorado 243,850 10,748 5,215 1,502 19,377,479Connecticut 236,498 9,016 5,833 1,888 18,569,623Delaware 72,159 2,537 2,220 336 5,649,899District of Columbia 2,697 104 72 15 212,346

Florida 1,054,831 43,759 22,662 8,665 83,308,715Georgia 749,052 34,489 14,858 4,494 59,737,751Hawaii 52,040 1,836 1,499 334 4,070,083Idaho 196,567 9,960 3,554 752 15,829,748Illinois 963,463 38,994 19,373 9,981 75,833,356Indiana 669,291 30,326 14,300 3,450 53,341,033Iowa 263,799 10,778 6,284 1,719 20,837,605Kansas 219,138 9,807 4,563 1,344 17,434,953Kentucky 672,499 26,714 16,317 4,732 52,992,981Louisiana 407,018 19,688 7,169 2,516 32,589,086Maine 291,537 8,409 9,830 2,064 22,525,881Maryland 279,384 11,693 6,575 1,665 22,117,057Massachusetts 590,847 18,772 18,041 4,535 45,875,064Michigan 1,016,738 41,768 25,924 4,819 80,451,316Minnesota 441,501 16,708 11,128 3,399 34,656,204Mississippi 263,644 11,245 5,544 2,025 20,873,756Missouri 849,802 34,601 21,447 4,493 67,171,213Montana 81,551 3,381 1,959 475 6,452,000Nebraska 172,392 8,097 3,301 1,015 13,772,061Nevada 120,628 5,239 2,687 706 9,576,862New Hampshire 121,971 5,535 2,410 808 9,711,429New Jersey 371,025 14,929 8,022 3,382 29,218,037New Mexico 126,902 5,853 2,772 511 10,136,825New York 1,753,381 54,271 56,815 11,534 136,049,088North Carolina 668,841 27,104 15,483 4,970 52,764,401North Dakota 52,578 2,002 1,292 426 4,127,625Ohio 1,329,951 54,418 33,692 6,690 105,181,441Oklahoma 422,119 20,717 7,243 2,543 33,844,244Oregon 415,236 14,518 12,533 2,226 32,483,559Pennsylvania 1,126,287 42,613 28,778 8,310 88,429,321Rhode Island 89,736 3,250 2,431 651 7,025,747South Carolina 405,429 15,754 9,297 3,656 31,850,997South Dakota 75,349 3,571 1,386 471 6,022,399Tennessee 1,060,363 34,329 32,718 7,283 82,470,274Texas 1,012,711 51,023 15,640 6,657 81,349,019Utah 214,958 9,305 5,445 660 17,096,922Vermont 93,595 3,030 2,754 770 7,271,901Virginia 368,434 16,182 7,278 2,888 29,232,790Washington 728,888 30,431 18,387 3,237 57,756,222West Virginia 352,864 13,628 9,390 2,019 27,778,282Wisconsin 544,950 18,574 14,208 5,446 42,414,222Wyoming 61,269 2,957 1,198 271 4,911,136

4.Number of individuals of unknown age in each state was allocated across all age groups based on the proportion of the number of individuals in an age group to total number of eligibles.

Note: Age groups differ slightly depending on source. Population age groups for Medicaid eligibles are 0-18, 19-64, and 65+ years, while National Health Interview Survey (NHIS) prevalence rates are 0-17, 18-64, and 65+ years.1. Centers for Medicare & Medicaid Services, MSIS State Summary FY 2004. Accessed at: http://www.cms.hhs.gov/MedicaidDataSourcesGenInfo/02_MSISData.asp

2. Based on data from the Centers for Disease Control and Prevention, Health Data for All Ages, National Center for Health Statistics, accessed at http://205.207.175.93/HDI/TableViewer/tableView.aspx?ReportId=60. Crude prevalence rates used when available.3.Calculations of direct cost per person, are based on Weiss et al. (2000), inflated by medical care component of CPI to 2004. Indirect cost per person are based on Weiss et al. (2000) inflated by percent change in average annual wages through 2004. Weiss KB, Sullivan SD, Lyttle CS. Trends in the cost of illness for asthma in the United States, 1985-1994. J Allergy Clin Immunol 2000 Sep;106(3):493-9.

85

Table B.2. Medicaid: Black eligibles by State and age group and estimated spending, 2004

Sta teBlack M edica id el igibles 20041

Black Medicaid e ligibles 0-18 with

asthma1,2,4

Black Medicaid eligibles 19-64 with

asthma1,2,4

Black Medicaid eligibles 65+ with

asthma1,2,4

Estimated Medicaid spending on asthma for Black eligibles3

Total U.S. 13,320,983 897,741 384,509 91,808 $1,524,534,326

Alabama 436,679 27,720 12,823 3,677 $49,063,076 Alaska 6,635 550 154 25 808,782Arizona 80,073 4,912 2,911 314 9,027,815Arkansas 209,923 15,086 5,793 1,197 24,492,888California 976,625 52,053 38,606 5,229 106,388,184Colorado 41,584 3,087 1,062 256 4,888,006Connecticut 110,945 7,124 3,269 886 12,513,440Delaware 70,292 4,162 2,584 327 7,846,812District of Co lumbia 139,053 9,049 4,445 774 15,830,668

Flo rida 844,911 59,032 21,686 6,941 97,259,455Georgia 855,834 66,368 20,282 5,134 101,836,077Hawaii 3,555 211 122 23 395,359Idaho 2,086 178 45 8 256,380Illino is 810,940 55,278 19,482 8,401 92,267,306Indiana 218,553 16,678 5,579 1,127 25,944,619Iowa 28,786 1,981 819 188 3,314,802Kansas 54,265 4,090 1,350 333 6,405,115Ken tucky 103,601 6,931 3,003 729 11,830,991Lou isiana 619,087 50,435 13,028 3,827 74,657,699Maine 7,123 346 287 50 758,220Maryland 442,491 31,192 12,442 2,638 51,338,533Massachusetts 126,942 6,793 4,631 974 13,755,236Mich igan 601,030 41,584 18,309 2,848 69,612,009Minnesota 118,215 7,535 3,560 910 13,319,380Mississippi 436,995 31,391 10,978 3,357 50,732,832Missouri 298,472 20,468 8,999 1,578 34,445,397Montana 1,005 70 29 6 116,359Nebraska 33,260 2,631 761 196 3,980,585Nevada 48,411 3,541 1,288 283 5,672,441New Hampsh ire 2,678 205 63 18 316,913New Jersey 307,191 20,818 7,936 2,801 35,009,687New Mexico 10,899 847 284 44 1,303,668New York 1,154,965 60,209 44,712 7,598 124,840,516North C arolina 609,834 41,621 16,865 4,532 69,919,408North D ako ta 1,419 91 42 12 159,948Oh io 589,261 40,608 17,835 2,964 68,131,329Oklahoma 107,827 8,913 2,211 650 13,062,440Oregon 25,900 1,525 934 139 2,882,482Pennsylvania 500,438 31,889 15,277 3,692 56,427,580Rhode Island 18,433 1,124 597 134 2,057,875Sou th Caro lina 483,878 31,668 13,257 4,363 54,685,278Sou th Dako ta 2,994 239 66 19 358,923Tennessee 450,537 24,566 16,608 3,095 49,117,024Texas 709,856 60,235 13,098 4,666 86,539,869Utah 5,969 435 181 18 703,558Vermont 1,300 71 46 11 141,220Virginia 366,039 27,076 8,638 2,869 42,809,081W ashington 71,648 5,038 2,159 318 8,338,618W est V irginia 19,798 1,288 629 113 2,252,795W isconsin 151,348 8,688 4,714 1,512 16,548,219W yoming 1,400 114 33 6 169,431

4. Number of individuals of unknow n age in each state was allocated acros s al l age groups based on the proportion of the number of individuals in an age group to total number of eligibles.

Note: Age groups di ffer s lightly depending on source. Population age groups for Medicaid eligibles are 0-18, 19-64, and 65+ years , whi le National Health Interview Survey (NHIS) prevalence rates are 0-17, 18-64, and 65+ years.1. Centers for Medicare & M edicaid Services, MSIS State Sum mary FY 2004. Accessed at: http://w ww.cm s.hhs.gov/MedicaidDataSourcesGenInfo/02_MSISData.asp2. Based on data from the Centers for Dis ease Control and Prevention, Health Data for All Ages, National Center for Health Statis tics, ac ces sed at http://205.207.175.93/HDI/TableViewer/tableView.aspx?ReportId=60. Crude prevalence rates used when available.

3.Calculations of direc t c ost per pers on, are bas ed on Weiss et al. (2000), inflated by m edical care component of CPI to 2004. Indirect cost per person are based on Weiss et al. (2000) inflated by perc ent change in average annual w ages through 2004. W eiss KB, Sull ivan SD, Lyttle CS. T rends in the cost of illness for asthm a in the U nited States, 1985-1994. J Al lergy Clin Immunol 2000 Sep;106(3) :493-9.

86

Table B.3. Medicaid: American Indian/Alaska Native eligibles by State and age group and estimated spending, 2004

State

American Indian/ Alaska Native

Medicaid eligibles 20041

American Indian/Alaska

Native Medicaid eligibles 0-18 with

asthma1,2,4

American Indian/Alaska

Native Medicaid eligibles 19-64 with

asthma1,2,4

American Indian/Alaska

Native Medicaid eligibles 65+ with

asthma1,2,4,5

Estimated Medicaid spending on asthma for American Indian/Alaska

Native eligibles3

Total U.S. 833,800 52,410 37,839 8,257 $109,293,210

Alabama 2,577 150 115 39 $337,392 Alaska 47,359 3,589 1,673 323 6,195,481Arizona 153,888 8,629 8,511 1,096 20,232,416Arkansas 5,599 368 235 58 733,214California 45,748 2,229 2,751 445 6,019,153Colorado 4,881 331 190 55 638,587Connecticut 986 58 44 14 129,115Delaware 344 19 19 3 45,223District of Columbia 47 3 2 0 6,165

Florida 2,904 185 113 43 379,689Georgia 1,492 106 54 16 195,071Hawaii 662 36 35 8 86,898Idaho 5,521 431 181 38 721,786Illinois 4,355 271 159 82 568,630Indiana 620 43 24 6 81,142Iowa 1,874 118 81 22 245,423Kansas 4,665 321 177 52 610,174Kentucky 432 26 19 6 56,577Louisiana 2,915 217 93 33 380,707Maine 3,924 174 240 50 516,140Maryland 1,575 101 67 17 206,277Massachusetts 2,895 142 161 40 380,160Michigan 9,332 590 432 80 1,223,813Minnesota 28,246 1,646 1,294 395 3,700,051Mississippi 2,961 194 113 41 387,129Missouri 4,006 251 184 38 525,194Montana 26,408 1,685 1,153 279 3,459,587Nebraska 9,073 656 316 97 1,185,917Nevada 3,722 249 151 40 487,205New Hampshire 160 11 6 2 20,914New Jersey 3,700 229 145 61 483,647New Mexico 96,357 6,842 3,826 705 12,618,225New York 82,849 3,948 4,879 991 10,892,731North Carolina 25,149 1,569 1,058 340 3,291,540North Dakota 16,389 961 732 242 2,145,931Ohio 2,113 133 97 19 277,054Oklahoma 87,102 6,581 2,717 954 11,373,991Oregon 14,440 777 792 141 1,897,367Pennsylvania 2,364 138 110 32 309,752Rhode Island 415 23 20 5 54,418South Carolina 1,589 95 66 26 207,839South Dakota 41,959 3,062 1,403 477 5,481,805Tennessee 2,623 131 147 33 344,586Texas 14,497 1,124 407 173 1,891,204Utah 10,629 708 489 59 1,394,578Vermont 245 12 13 4 32,150Virginia 1,349 91 48 19 176,259Washington 30,407 1,954 1,394 245 3,987,521West Virginia 132 8 6 1 17,314Wisconsin 14,461 759 685 263 1,893,597Wyoming 5,860 435 208 47 766,470

4.Number of individuals of unknown age in each state was allocated across all age groups based on the proportion of the number of individuals in an age group to total number of eligibles.

Note: Age groups differ slightly depending on source. Population age groups for Medicaid eligibles are 0-18, 19-64, and 65+ years, while National Health Interview Survey (NHIS) prevalence rates are 0-17, 18-64, and 65+ years.

5. Asthma prevalence rate unavailable for American Indian/Alaska native population age 65+, and was estimated from the American Indian /Alaska Native to White ratio of prevalence for ages 19-64 multiplied by the prevalence rate for Whites ages 65+.

2. Based on data from the Centers for Disease Control and Prevention, Health Data for All Ages, National Center for Health Statistics, accessed at http://205.207.175.93/HDI/TableViewer/tableView.aspx?ReportId=60. Crude prevalence rates used when available.

3.Calculations of direct cost per person, are based on Weiss et al. (2000), inflated by medical care component of CPI to 2004. Indirect cost per person are based on Weiss et al. (2000) inflated by percent change in average annual wages through 2004. Weiss KB, Sullivan SD, Lyttle CS. Trends in the cost of illness for asthma in the United States, 1985-1994. J Allergy Clin Immunol 2000 Sep;106(3):493-9.

1.Centers for Medicare & Medicaid Services, MSIS State Summary FY 2004. Accessed at: http://www.cms.hhs.gov/MedicaidDataSourcesGenInfo/02_MSISData.asp

87

Table B.4. Medicaid: Asian eligibles by State and age group and estimated spending, 2004

StateAsian Medicaid eligibles 20041

Asian Medicaid eligibles 0-18 with

asthma1,2,4

Asian Medicaid eligibles 19-64 with

asthma1,2,4

Asian Medicaid eligibles 65+ with

asthma1,2,4

Estimated Medicaid spending on asthma for Asian eligibles3

Total U.S. 1,502,416 30,914 20,915 8,251 $66,659,132

Alabama 4,240 93 51 31 $193,321 Alaska 6,523 186 61 21 298,141Arizona 17,422 367 257 60 759,018Arkansas 6,669 165 75 33 302,511California 515,864 9,451 8,279 2,411 22,347,290Colorado 6,779 173 70 36 310,392Connecticut 11,860 262 142 83 539,549Delaware 2,521 51 38 10 110,023District of Columbia 1,375 31 18 7 61,342

Florida 23,431 563 244 168 1,081,722Georgia 22,350 596 215 117 1,029,496Hawaii 68,483 1,399 957 384 3,038,842Idaho 1,181 35 10 4 54,312Illinois 54,523 1,278 532 493 2,554,590Indiana 4,009 105 42 18 182,797Iowa 3,559 84 41 20 161,493Kansas 4,454 115 45 24 204,411Kentucky 2,920 67 34 18 132,535Louisiana 6,173 173 53 33 287,271Maine 2,761 46 45 17 120,186Maryland 24,435 592 279 127 1,107,505Massachusetts 38,633 711 572 259 1,710,438Michigan 26,120 621 323 108 1,167,567Minnesota 46,631 1,022 570 313 2,113,829Mississippi 3,972 98 41 27 183,321Missouri 8,483 200 104 39 380,527Montana 472 11 6 2 21,335Nebraska 2,825 77 26 15 130,448Nevada 8,269 208 89 42 376,664New Hampshire 1,048 28 10 6 48,417New Jersey 20,609 480 216 164 954,477New Mexico 2,889 77 31 10 130,822New York 286,679 5,137 4,506 1,646 12,525,665North Carolina 14,506 340 163 94 662,716North Dakota 270 6 3 2 12,295Ohio 13,587 322 167 60 608,547Oklahoma 6,837 194 57 36 318,576Oregon 17,272 350 253 81 758,156Pennsylvania 36,286 795 450 234 1,640,156Rhode Island 5,073 106 67 32 227,636South Carolina 3,126 70 35 25 143,907South Dakota 714 20 6 4 33,127Tennessee 3,938 74 59 24 173,486Texas 56,696 1,654 425 325 2,667,052Utah 8,844 222 109 24 392,754Vermont 406 8 6 3 18,107Virginia 21,394 544 205 146 993,438Washington 49,394 1,194 604 192 2,207,711West Virginia 541 12 7 3 24,167Wisconsin 25,093 495 317 219 1,144,355Wyoming 277 8 3 1 12,690

4.Number of individuals of unknown age in each state was allocated across all age groups based on the proportion of the number of individuals in an age group to total number of eligibles.

Note: Age groups differ slightly depending on source. Population age groups for Medicaid eligibles are 0-18, 19-64, and 65+ years, while National Health Interview Survey (NHIS) prevalence rates are 0-17, 18-64, and 65+ years.1.Centers for Medicare & Medicaid Services, MSIS State Summary FY 2004. Accessed at: http://www.cms.hhs.gov/MedicaidDataSourcesGenInfo/02_MSISData.asp2. Based on data from the Centers for Disease Control and Prevention, Health Data for All Ages, National Center for Health Statistics, accessed at http://205.207.175.93/HDI/TableViewer/tableView.aspx?ReportId=60. Crude prevalence rates used when available.

3.Calculations of direct cost per person, are based on Weiss et al. (2000), inflated by medical care component of CPI to 2004. Indirect cost per person are based on Weiss et al. (2000) inflated by percent change in average annual wages through 2004. Weiss KB, Sullivan SD, Lyttle CS. Trends in the cost of illness for asthma in the United States, 1985-1994. J Allergy Clin Immunol 2000 Sep;106(3):493-9.

88

Table B.5. Medicaid: Hispanic eligibles by State and age group and estimated spending, 2004

StateHispanic or Latino Medicaid eligibles

20041

Hispanic or Latino Medicaid eligibles 0-18 with asthma1,2,4

Hispanic or Latino Medicaid eligibles

19-64 with asthma1,2,4

Hispanic or Latino Medicaid eligibles

65+ with asthma1,2,4

Estimated Medicaid spending on asthma for

Hispanic eligibles3

Total U.S. 12,533,000 425,103 221,241 62,538 $786,512,211

Alabama 20,835 713 330 145 $1,318,164 Alaska 4,699 210 59 15 314,460Arizona 623,427 20,616 12,235 2,016 38,685,079Arkansas 33,852 1,311 504 159 2,191,261California 5,787,987 166,296 123,514 25,580 349,928,352Colorado 197,336 7,897 2,722 1,003 12,894,532Connecticut 147,891 5,119 2,352 974 9,370,640Delaware 21,285 679 422 82 1,312,980District of Columbia 12,765 448 220 59 806,331

Florida 714,828 26,923 9,905 4,847 46,238,378Georgia 26,314 1,100 337 130 1,738,559Hawaii 8,157 261 151 43 505,888Idaho 14,841 683 173 47 1,001,592Illinois 402,105 14,775 5,215 3,438 25,994,235Indiana 79,022 3,251 1,089 336 5,187,979Iowa 14,385 534 221 77 923,082Kansas 34 1 0 0 2,230Kentucky 17,859 644 279 104 1,139,761Louisiana 8,984 395 102 46 601,830Maine 1,052 28 23 6 62,768Maryland 65,393 2,485 993 322 4,215,298Massachusetts 188,707 5,443 3,716 1,195 11,488,797Michigan 97,695 3,644 1,607 382 6,249,236Minnesota 702 24 11 4 44,372Mississippi 6,636 257 90 42 431,635Missouri 62 2 1 0 3,963Montana 3,629 137 56 17 233,297Nebraska 97 4 1 0 6,441Nevada 57,862 2,281 831 279 3,763,624New Hampshire 4,691 193 60 26 309,220New Jersey 175,870 6,425 2,453 1,323 11,317,942New Mexico 264,030 11,056 3,720 877 17,367,954New York 663,942 18,658 13,876 3,605 40,095,965North Carolina 77,777 2,861 1,161 477 4,992,505North Dakota 0 0 0 0 0Ohio 60,185 2,236 983 250 3,848,924Oklahoma 0 0 0 0 0Oregon 109,713 3,483 2,136 485 6,772,371Pennsylvania 138,164 4,746 2,277 841 8,725,497Rhode Island 39,218 1,290 685 235 2,451,681South Carolina 25,604 903 379 191 1,633,797South Dakota 2,660 114 32 14 177,230Tennessee 41,376 1,216 823 235 2,523,190Texas 2,045,169 93,550 20,371 11,096 138,708,065Utah 52,331 2,057 855 133 3,377,417Vermont 303 9 6 2 18,544Virginia 59,191 2,360 754 383 3,880,290Washington 162,553 6,162 2,645 596 10,431,803West Virginia 30 1 1 0 1,896Wisconsin 50,166 1,552 844 414 3,117,426Wyoming 1,586 69 20 6 105,730

4.Number of individuals of unknown age in each state was allocated across all age groups based on the proportion of the number of individuals in an age group to total number of eligibles.

Note: Age groups differ slightly depending on source. Population age groups for Medicaid eligibles are 0-18, 19-64, and 65+ years, while National Health Interview Survey (NHIS) prevalence rates are 0-17, 18-64, and 65+ years.1. Centers for Medicare & Medicaid Services, MSIS State Summary FY 2004. Accessed at: http://www.cms.hhs.gov/MedicaidDataSourcesGenInfo/02_MSISData.asp2. Based on data from the Centers for Disease Control and Prevention, Health Data for All Ages, National Center for Health Statistics, accessed at http://205.207.175.93/HDI/TableViewer/tableView.aspx?ReportId=60. Crude prevalence rates used when available.3.Calculations of direct cost per person, are based on Weiss et al. (2000), inflated by medical care component of CPI to 2004. Indirect cost per person are based on Weiss et al. (2000) inflated by percent change in average annual wages through 2004. Weiss KB, Sullivan SD, Lyttle CS. Trends in the cost of illness for asthma in the United States, 1985-1994. J Allergy Clin Immunol 2000 Sep;106(3):493-9.

89

Table B.6. Medicaid: Other eligibles by State and age group and estimated spending, 2004

StateOther Medicaid eligibles 20041

Other Medicaid eligibles 0-18 with

asthma1,2

Other Medicaid eligibles 19-64 with

asthma1,2

Other Medicaid eligibles 65+ with

asthma1,2

Estimated Medicaid spending on asthma for Other eligibles3

Total U.S. 3,536,913 219,581 186,648 25,581 $479,097,756

Alabama 21,426 1,403 992 201 $2,880,110 Alaska 5,127 438 188 20 717,108Arizona 33,724 2,133 1,933 131 4,657,050Arkansas 0 0 0 0 0California 630,939 34,679 39,323 3,525 86,016,952Colorado 23,944 1,833 965 155 3,275,614Connecticut 207 14 10 2 27,865Delaware 3 0 0 0 412District of Columbia 4,367 293 220 26 598,512

Florida 226,456 16,316 9,164 1,915 30,396,366Georgia 103,253 8,257 3,858 656 14,169,484Hawaii 2,111 129 115 14 286,495Idaho 0 0 0 0 0Illinois 23,520 1,653 891 264 3,116,012Indiana 10,636 837 428 60 1,470,068Iowa 75,722 5,373 3,397 538 10,328,744Kansas 11,255 875 441 73 1,541,248Kentucky 36,004 2,484 1,646 278 4,890,736Louisiana 66,064 5,550 2,192 440 9,077,689Maine 0 0 0 0 0Maryland 31,629 2,299 1,402 197 4,325,026Massachusetts 208,666 11,515 12,001 1,793 28,080,765Michigan 19,343 1,380 929 98 2,670,929Minnesota 36,411 2,393 1,729 305 4,911,581Mississippi 70,879 5,251 2,807 596 9,601,963Missouri 44,156 3,123 2,099 254 6,075,560Montana 8 1 0 0 1,099Nebraska 6,316 515 228 40 868,824Nevada 0 0 0 0 0New Hampshire 3,668 289 136 27 501,985New Jersey 110,207 7,702 4,489 1,076 14,719,167New Mexico 10,701 857 440 46 1,490,357New York 947,125 50,917 57,810 6,766 128,140,396North Carolina 124,180 8,740 5,415 989 16,802,578North Dakota 5 0 0 0 675Ohio 968 69 46 5 133,489Oklahoma 0 0 0 0 0Oregon 7,056 428 401 41 966,302Pennsylvania 41,161 2,705 1,981 323 5,557,405Rhode Island 63,177 3,974 3,224 497 8,538,546South Carolina 71,032 4,794 3,068 671 9,467,573South Dakota 25 2 1 0 3,432Tennessee 49,151 2,764 2,857 344 6,618,082Texas 39,254 3,435 1,142 276 5,384,243Utah 1,552 117 74 5 217,149Vermont 67,746 3,809 3,755 593 9,051,342Virginia 1,881 143 70 16 254,182Washington 147,988 10,731 7,032 690 20,474,176West Virginia 8 1 0 0 1,095Wisconsin 157,386 9,317 7,729 1,631 20,723,109Wyoming 476 40 18 2 66,260

Note: Age groups differ slightly depending on source. Population age groups for Medicaid eligibles are 0-18, 19-64, and 65+ years, while National Health Interview Survey (NHIS) prevalence rates are 0-17, 18-64, and 65+ years.1. Centers for Medicare & Medicaid Services, MSIS State Summary FY 2004. Other includes individuals in the categories of More than One Race, Unknown/Multiple Responses, and Native Hawaiian and Other Pacific Islander. Accessed at: http://www.cms.hhs.gov/MedicaidDataSourcesGenInfo/02_MSISData.asp2. Based on data from the Centers for Disease Control and Prevention, Health Data for All Ages, National Center for Health Statistics, accessed at http://205.207.175.93/HDI/TableViewer/tableView.aspx?ReportId=60. Crude prevalence rates used when available.

3.Calculations of direct cost per person, are based on Weiss et al. (2000), inflated by medical care component of CPI to 2004. Indirect cost per person are based on Weiss et al. (2000) inflated by percent change in average annual wages through 2004. Weiss KB, Sullivan SD, Lyttle CS. Trends in the cost of illness for asthma in the United States, 1985-1994. J Allergy Clin Immunol 2000 Sep;106(3):493-9.

90

91

Calculate a weighted average of age 18-44

and 45-64 to get prevalence rate for age

18-64 within race

Apply age group proportion of Medicaid

population in each State to each subgroup (assumes age distribution of each

racial subgroup is the same within the State Medicaid

population)

Inflate to 2003 dollars using Medical care

component of CPI

Inflate to 2003 dollars using average annual wage percent

change

Direct cost per person

with asthma 2003

Indirect cost per person

with asthma 2003

Multiply prevalence rate by Medicaid

population age and race/ethnicity

subgroup to get number of Medicaid

eligibles by State, age and

race/ethnicity

Number of Medicaid eligibles with asthma in each age/race subgroup for each

State

Multiply number in each age/race subgroup by total

annual expenditures per person with asthma

National prevalence rates by age (0-17, 18-64, 65+) within race/ethnicity

Medicaid eligibles by State, age within

race/ethnicity 2003

Sum of direct cost and indirect cost

to get total expenditures per

person with asthma, 2003

Figure B.1. Methods for deriving Medicaid spending for asthma by State, age, and race/ethnicity, 2003

Total spending in 2003 for Medicaid eligibles with asthma age/race subgroup

by each State

Original source data

Adjust-ments

Final Estimate

National annual average asthma prevalence 2003, by age 0-17, 18-44, 45-64, 65+ within race/ethnicity (NHIS, 1999-2003)

Population estimates for 2003 by age (US Census data, 2004)

Medicaid eligibles

population by age 2003

(CMS, MSIS data, 2003)

Medicaid eligibles

population by race 2003

(CMS, MSIS data, 2003)

Direct asthma medical

expenditures per person with

asthma for 1994 (Weiss et al,

2000)

Indirect expenditures

per person with asthma for 1994

(Weiss et al, 2000)

Appendix C: National and State Asthma Programs This appendix lists State asthma programs in existence in the spring of 2005. Program information can be obtained from the Web linkages provided in the table. The programs are classified by the following typology:

• Advisory Bodies and Councils • Coalitions • Collaboratives • Cross-Agency Work • Data Measurement/Reporting • Developing and Enforcing Guidelines • Disease Management • Minority and Rural Outreach • Public Service and Education • Self-Management • Provider Training

92

Location Program Classification & Year Begun For More Information

National American Lung Association Education 1904

www.lungusa.org

Association of Asthma Educators Provider Training 2002

www.asthmaeducators.org

Asthma and Allergy Foundation of America Education 1953

http://aafa.org/index.cfm

National Asthma Education and Prevention Program

Developing Guidelines March 1989

www.nhlbi.nih.gov

National Asthma Education Certification Board Provider Training 2000

http://www.naecb.org/

National Respiratory Training Center Provider Training 2002

http://www.nrtc-usa.org/

Asthma Indicator Screenings Disease Management http://www.lungusa2.org/alabama/programs.html#one

Alabama

Inner-City Asthma Intervention – University of Alabama at Birmingham, School of Medicine (CDC funded)

Self-Management April 2001

http://www.health.uab.edu/show.asp?durki=10597

Assessing Alaska's Asthma Burden (CDC funded)

Coalitions October 2003

http://www.aklung.org/asthmacoalition.htm Alaska

Statewide Asthma Education Programs (CDC funded)

Self-Management Patient Education October 2003

http://aafa.org/display.cfm?id=10&sub=88&cont=201

Inner-City Asthma Intervention – St. Joseph’s Hospital and Medical Center (CDC funded)

Self-Management April 2001

lparrar@chw.edu

Arizona

Inner-City Asthma Intervention – El Rio Health Center (CDC funded)

Self-Management April 2001

http://www.elrio.org/ http://www.elrio.org/initiatives/asthma.htm

93

Arkansas Arkansas Asthma Coalition – Arkansas Children's Hospital Research Institute

Coalitions Minority and Rural Outreach 1997

http://achri.ach.uams.edu/ http://hp2010.nhlbihin.net/ast_maps/arkansas.html

Better Asthma Care for California Kids Education Provider Training 2005

http://www.betterasthmacare.org/ http://www.dhs.ca.gov/ps/cdic/caphi/

Inner-City Asthma Intervention – Children’s Asthma Consortium (CDC funded)

Self-Management April 2001

http://www.mssm.edu/peds

California Breathing – California Department of Health Services

Coalitions Self-Management Patient Education

http://www.dhs.ca.gov/cdic/caphi/default.htm http://www.californiabreathing.org/

Mobile Asthma Care – Children's Hospital Central California (CDC funded)

Self-Management Patient Education August 2002

http://www.cdc.gov/asthma/contacts/ca.htm

Replication and Implementation of Scientifically Proven Asthma Interventions – Chula Vista Elementary School District (CDC funded)

Self-Management Disease Management April 2001

http://www.cvesd.k12.ca.us

Controlling Asthma in American Cities – University of California, Berkeley (CDC funded)

Collaboratives September 2001

http://www.berkeley.edu

CalAsthma

Disease Management February 2001

http://www.calasthma.org/

California

California Statewide Collaborative for Achieving Better Care for Asthma (Medicaid contract)

Coalitions Collaboratives 1995

Center for Health Care Strategies http://www.chcs.org/

The Colorado Asthma Program (CDC funded) Collaboratives Disease Management Self-Management Data Measurement/ Reporting September 2001

cdphe.psdrequests@state.co.us http://www.cdphe.state.co.us/ps/asthma/asthmahom.asp

Colorado

Colorado Asthma Coalition Coalitions October 2000

asthmacoalition@alacolo.org http://www.asthmacolorado.org/

94

National Jewish Pilot Disease Management Program (Medicaid contract)

Disease Management February 2004

http://www.nationaljewish.org/news/health-news/y2004/medicaid-co-results.aspx

Connecticut Addressing Asthma from a Public Health Perspective – Connecticut Department of Public Health and Addiction Services (CDC funded)

Cross-Agency Work Data Measurement September 2001

http://www.dph.state.ct.us/BCH/new_asthma/asthma_task_force.htm

Delaware No programs found Inner-City Asthma Intervention Health Choice Network (CDC funded)

Self-Management April 2001

http://www.cdc.gov/asthma/contacts/fl.htm

Population-Based Models To Establish Surveillance for Asthma Incidence in Defined Geographic Areas – Miami-Dade County Health Department (CDC funded)

Data Measurement/ Reporting Disease Management September 2001

http://www.dadehealth.org/

Florida

Medicaid Disease Management Programs (Medicaid funded)

Disease Management

http://www.fdhc.state.fl.us/Medicaid/Disease_Management/index.shtml

Asthma in Older Women – Kaiser Permanente Georgia (CDC funded)

Data Measurement/ Reporting October 2000

http://www.cdc.gov/asthma/contacts/ga.htm

Addressing Asthma from a Public Health Perspective – Georgia Department of Human Resources (CDC funded)

Cross-Agency Work Data Measurement/ Reporting September 2001

http://health.state.ga.us/pdfs/epi/cdiee/burdenofasthma.03.pdf http://health.state.ga.us/pdfs/epi/cdiee/AsthmaStrategicPlan_2004.pdf

Georgia

Viruses and Asthma – Emory University School of Medicine (CDC funded)

Coalitions September 2003

http://medicine.emory.edu/ http://www.sph.emory.edu/zapasthma/overview.htm

Assessment of Health Effects Associated with Volcanic Emissions – The Hawaii State Department of Health (CDC funded)

Data Measurement/ Reporting Disease Management September 1998

http://www.state.hi.us/doh/

Addressing Asthma from a Public Health Perspective – The Hawaii State Department of Health (CDC funded)

Cross-Agency Work Data Measurement/ Reporting September 2002

http://www.state.hi.us/doh/

Hawaii

Childhood Rural Asthma Project, Phase 2 – The Hawaii State Department of Health (CDC funded)

Minority and Rural Outreach September 2003

http://www.state.hi.us/doh/

95

Idaho Addressing Asthma from a Public Health Perspective – Idaho Department of Health and Welfare (CDC funded) Other programs: • Healthy Homes, Head Start • School Asthma Management Model for Idaho (SAMMI)

Cross-Agency Work Data Measurement/ Reporting September 2001

http://www2.state.id.us/dhw/asthma/home.htm

Addressing Asthma in Illinois-Statewide program

Coalitions Developing Guidelines July 2000

http://www.idph.state.il.us/pdf/addressing_asthma.pdf

Chicago Asthma Consortium Coalitions 1996

http://www.chicagoasthma.org/

Replication and Implementation of Scientifically Proven Asthma Interventions American Lung Association of Metropolitan Chicago (CDC funded)

Disease Management Self-Management September 2001

http://www.lungchicago.org/

Inner-City Asthma Intervention – Cook County Hospital, Department of Pediatrics, Pediatric Allergy Division Office (CDC funded)

Self-Management April 2001

http://www.bronx-leb.org

Replication and Implementation of Scientifically Proven Asthma Interventions – PCC Community Wellness Center (CDC funded)

Disease Management Self-Management September 2002

http://www.cdc.gov/asthma/contacts/il.htm http://www.pccwellness.org/contact.htm

Replication and Implementation of Scientifically Proven Asthma Interventions – Southern Illinois Healthcare Foundation (CDC funded)

Disease Management Self-Management September 2002

http://www.cdc.gov/asthma/contacts/il.htm

Illinois

Controlling Asthma in American Cities – University of Illinois (CDC funded)

Collaboratives September 2001

http://www.uic.edu/

Indiana Addressing Asthma from a Public Health Perspective – Indiana State Department of Health (CDC funded)

Cross-Agency Work Data Measurement/ Reporting September 2002

http://www.state.in.us/isdh/index.htm

96

Indiana Chronic Disease Management Program Disease Management Early 2004

http://www.indianacdmprogram.com/ http://www.indianacdmprogram.com/Member/asthma.htm

Iowa Addressing Asthma from a Public Health Perspective – Iowa Department of Public Health (CDC funded)

Coalitions Data Measurement August 2000

http://www.idph.state.ia.us/hpcdp/common/pdf/asthma/asthma_surveillance_plan.pdf

Kansas American Lung Association of Kansas Disease Management http://www.kslung.org/programs/asthmaprog.html Good Health Kentucky Asthma Resource – Good Samaritan Foundation

Public Service and Education

http://www.goodhealthky.org/asthma.html Kentucky

The Metropolitan Asthma Coalition (MAC) – American Lung Association, Louisville, Kentucky

Developing and Enforcing Guidelines

http://www.kylung.org/mac.html

Louisiana American Lung Association of Louisiana Public Service and Education

http://www.louisianalung.org/

Maine Addressing Asthma from a Public Health Perspective – Maine Asthma Prevention and Control Program (CDC funded)

Cross-Agency Work Data Measurement August 2000

http://www.maine.gov/dhhs/bohdcfh/mat/

Maryland Maryland Childhood Asthma Program, Dept. of Health and Mental Hygiene Addressing Asthma from a Public Health Perspective (CDC funded)

Cross-Agency Work Data Measurement/ Reporting Developing Guidelines September 2001

http://www.fha.state.md.us/mch/asthma/ http://www.fha.state.md.us/mch/pdf/Asthma_in_Maryland_2003.pdf

Baystate Medical Center – Inner-City Asthma Intervention (CDC funded)

Self-Management April 2001

http://www.baystatehealth.com/bmc/ Massachusetts

Massachusetts Department of Public Health Addressing Asthma from a Public Health Perspective (CDC funded)

Cross-Agency Work Data Measurement/ Reporting September 2003

http://www.state.ma.us/dph/

Michigan Addressing Asthma from a Public Health Perspective – Michigan Department of Community Health (CDC funded)

Cross-Agency Work Data Measurement/ Reporting August 2000

http://www.GetAsthmaHelp.org/

97

Asthma Surveillance and Interventions in Hospital Emergency Departments Program (CDC funded)

Collaboratives August 2001

http://www.msu.edu/

Enhanced Surveillance of Asthma Deaths Michigan Department of Community Health (CDC funded)

Collaboratives September 2000

http://www.GetAsthmaHelp.org/

Controlling Asthma in American Cities American Lung Association of Minnesota (CDC funded)

Collaboratives April 2001

http://www.alamn.org/InfoCenter/default.asp http://www.alamn.org/InfoCenter/ProviderDefault.asp

Inner-City Asthma Intervention – American Lung Association of Minnesota (CDC funded)

Self-Management April 2001

http://www.alamn.org/InfoCenter/default.asp http://www.alamn.org/InfoCenter/ProviderDefault.asp

Minnesota

Addressing Asthma from a Public Health Perspective – Minnesota Department of Health (CDC funded)

Advisory Bodies Cross-Agency Work Data Measurement/ Reporting September 1999

http://www.health.state.mn.us/divs/hpcd/cdee/asthma/

Inner-City Asthma Intervention – Jackson-Hinds Comprehensive Health Center

Self-Management April 2001

(601) 362-5321

Mississippi

Addressing Asthma from a Public Health Perspective – Mississippi Department of Health

Cross-Agency Work Data Measurement/ Reporting September 2003

http://www.msdh.state.ms.us/msdhsite/index.cfm

Inner-City Asthma Intervention Children’s Mercy Hospitals and Clinics

Self-Management April 2001

http://www.childrensmercy.org

Inner-City Asthma Intervention – Washington University School of Medicine

Self-Management April 2001

http://medicine.wustl.edu/

Addressing Asthma from a Public Health Perspective – Missouri Department of Health and Senior Services (CDC funded)

Cross-Agency Work Data Measurement/ Reporting September 2001

http://www.dhss.mo.gov/asthma/asthmastateplan.pdf

Missouri

Controlling Asthma in American Cities – St. Louis Regional Asthma Consortium (CDC funded)

Collaboratives Fall 2000

http://www.asthma-stlouis.org/

98

Missouri State Medicaid Disease Management Program (Medicaid funded)

Disease Management November 2002

http://www.heritage-info.com/mocaidrx/files/dm/Provider_Handbook.doc

Montana Montana Environmental Public Health Tracking Program -- Montana Department of Public Health and Human Services (CDC National EPHT Program)

Data Measurement/ Reporting

http://www.dphhs.mt.gov/epht/asthma.shtml

Nebraska Addressing Asthma from a Public Health Perspective – Nebraska Health and Human Services System (CDC funded)

Cross-Agency Work Data Measurement/ Reporting September 2001

http://www.hhs.state.ne.us/epi/asthma.htm

Nevada American Lung Association of Nevada Public Service and Education

http://www.lungs.org/

New Hampshire

Addressing Asthma from a Public Health Perspective – New Hampshire Department of Health and Human Services (CDC funded)

Cross-Agency Work Data Measurement/ Reporting

http://www.dhhs.nh.gov/DHHS/CDPC/asthma.htm

Interdepartmental Report and Strategic Plan for Asthma – New Jersey Department of Health and Senior Services and others

Collaboratives/ Coalitions Minority and Rural Outreach

http://www.state.nj.us/health/commiss/omh/asthma/asthmastrategicplan.pdf

Replication and Implementation of Scientifically Proven Asthma Interventions –Babyland Family Services (CDC funded)

Self-Management Disease Management September 2001

http://www.nccic.org/ccpartnerships/profiles/babyland.htm

Inner-City Asthma Intervention – Children’s Hospital of New Jersey at Newark Beth Israel Medical Center, Saint Barnabas Health Care System (CDC funded)

Self-Management Minority and Rural Outreach April 2001

http://www.sbhcs.com/hospitals/newark_beth_israel/

Replication and Implementation of Scientifically Proven Asthma Interventions – PBI Regional Medical Center (CDC funded)

Self-Management Disease Management September 2002

http://www.cdc.gov/asthma/contacts/nj.htm http://www.pbih.org

New Jersey

Addressing Asthma from a Public Health Perspective – New Jersey Department of Health and Senior Services (CDC funded)

Cross-Agency Work Data Measurement/ Reporting

http://www.state.nj.us/health/fhs/asthma.shtml

99

New Mexico Addressing Asthma from a Public Health Perspective – New Mexico Department of Health (CDC funded)

Cross-Agency Work Data Measurement/ Reporting August 2000

http://www.health.state.nm.us/

New York's Action Against Asthma: • Regional Asthma Coalitions • Healthy Neighborhoods Program • Minority Asthma Partnerships • Medicaid • Child Health Plus • Family Health Plus • School Based Health Centers

Cross-Agency Work Data Measurement/ Reporting Coalitions Public Service and Education

http://www.health.state.ny.us/ http://www.health.state.ny.us/nysdoh/asthma/ny_action.htm http://www.health.state.ny.us/nysdoh/asthma/pdf/4850.pdf

Replication and Implementation of Scientifically Proven Asthma Intervention – Bronx-Lebanon Hospital Center (CDC funded)

Self-Management Disease Management September 2001

http://www.bronx-leb.org/ http://www.bronx-leb.org/Centers_of_Excellence/Asthma.asp

Controlling Asthma in American Cities –Columbia University (CDC funded)

Collaboratives September 2001

http://www.columbia.edu/ http://www.cumc.columbia.edu/dept/pulmonary/4ClinicalPage/Clinical%20Centers/Website/main.htm

Inner-City Asthma Intervention – Mount Sinai School of Medicine Department of Pediatrics (CDC funded)

Self-Management April 2001

http://www.mssm.edu/peds/

New York

Inner-City Asthma Intervention – University at Buffalo, SUNY (CDC funded)

Self-Management April 2001

http://www.smbs.buffalo.edu/fam-med/

Inner-City Asthma Intervention – WakeMed (CDC funded)

Self-Management April 2001

http://www.wakemed.org North Carolina

Assessing Asthma-Related School and Work Absences – University of North Carolina-Chapel Hill, School of Public Health (CDC funded)

Data Measurement/ Reporting October 2001

http://www.hpdp.unc.edu/Research/AccessingAsthma.pdf?CFID=50420&CFTOKEN=52829508

North Dakota No programs listed Ohio Inner-City Asthma Intervention

Rainbow Babies and Children’s Hospital (CDC funded)

Self-Management April 2001

http://www.uhrainbow.com/

100

The Ohio Asthma Coalition Coalitions http://www.ohiolung.org/ohio-asthma-coalition.htm Oklahoma Addressing Asthma from a Public Health

Perspective – Oklahoma State Department of Health (CDC funded)

Cross-Agency Work Data Measurement/ Reporting September 2002

http://www.health.state.ok.us/

Oregon Asthma Program – Oregon DHS Oregon Asthma Network

Coalitions Self-Management Public Service and Education 2000

http://www.dhs.state.or.us/publichealth/asthma/index.cfm http://www.dhs.state.or.us/publichealth/asthma/plan/provider.cfm http://oregon.gov/DHS/ph/asthma/guideor.shtml

Inner-City Asthma Intervention – CareOregon (CDC funded)

Self-Management April 2001

http://www.careoregon.org/member/masthma.html

Addressing Asthma from a Public Health Perspective – Oregon Department of Human Services (CDC funded)

Cross-Agency Work Data Measurement/ Reporting October 1999

http://oregon.gov/DHS/ph/asthma/index.shtml

Asthma in Older Women – Kaiser Northwest/Kaiser Colorado

Data Measurement/ Reporting

http://www.cdc.gov/asthma/contacts/or.htm

Incidence of Occupational Asthma within the Northwest Division of Kaiser Permanente – Kaiser Foundation Research Institute

Data Measurement/ Reporting

http://www.cdc.gov/asthma/contacts/or.htm (503) 335-6755

Oregon

Population-Based Models To Establish Surveillance for Asthma Incidence in Defined Geographic Areas – Kaiser Foundation Research Institute

Developing and Enforcing Guidelines

http://www.cdc.gov/asthma/contacts/or.htm (503) 335-6755

Controlling Asthma in American Cities –Children’s Hospital of Philadelphia (CDC funded)

Collaboratives http://www.chop.edu/consumer/index.jsp Pennsylvania

Addressing Asthma from a Public Health Perspective – Pennsylvania Department of Health (CDC funded)

Cross-Agency Work Data Measurement/ Reporting September 2001

http://webserver.health.state.pa.us/health/site/

101

Replication and Implementation of Scientifically Proven Asthma Interventions – Philadelphia Department of Public Health

Self-Management Disease Management September 2003

http://www.phila.gov/health/

National Jewish Medical and Research Center Disease Management http://www.nationaljewish.org/disease-info/diseases/asthma/index.aspx

Rhode Island Rhode Island Asthma Control Program – Rhode Island Department of Health (CDC funded)

Developing and Enforcing Guidelines September 1999

http://www.health.state.ri.us/disease/asthma/index.php

South Carolina Asthma Surveillance and Interventions in Hospital Emergency Departments Program – University of South Carolina (CDC funded)

Coalitions Public Service and Education Data Measurement/ Reporting September 2000

http://www.sc.edu/

South Dakota No programs listed Tennessee Asthma Care Management Program –Tennessee

Department of Finance & Administration Public Service and Education Self-Management Disease Management

http://www.state.tn.us/tenncare/

The Asthma Coalition of Texas Coalitions http://www.texasasthma.org/ Replication and Implementation of Scientifically Proven Asthma Interventions – Harris County Hospital District (CDC funded)

Self-Management Disease Management September 2001

http://www.hchdonline.com/

Inner-City Asthma Intervention – University of Texas Health Science Center at San Antonio (CDC funded)

Self-Management April 2001

http://www.uthscsa.edu

Addressing Asthma from a Public Health Perspective – Texas Department of Health (CDC funded)

Cross-Agency Work Data Measurement/ Reporting September 2001

http://www.dshs.state.tx.us/ http://www.dshs.state.tx.us/chronic/pdf/TAR.pdf

Texas

Texas Medicaid Managed Care Asthma Project Self-Management Disease Management

http://www.hhsc.state.tx.us/medicaid/mc/proj/asthma/asthma.html

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103

Utah Addressing Asthma from a Public Health Perspective – Utah Department of Health (CDC funded)

Cross-Agency Work Data Measurement/ Reporting September 2001

http://health.utah.gov/asthma/

Vermont Addressing Asthma from a Public Health Perspective – Vermont Dept. of Health Asthma Program (CDC funded)

Cross-Agency Work Data Measurement/ Reporting August 2000

http://healthvermont.gov/prevent/asthma/index.aspx

Controlling Asthma in Richmond Metropolitan Area (CARMA) (CDC funded)

Collaboratives September 2001

http://www.carmakids.org/ Virginia

Addressing Asthma from a Public Health Perspective – Virginia Department of Health (CDC funded)

Cross-Agency Work Data Measurement/ Reporting September 2001

http://www.vahealth.org/asthma/

Replication and Implementation of Scientifically Proven Asthma Interventions – Asthma and Allergy Foundation of America Washington State Chapter (CDC funded)

Self-Management Disease Management

http://www.aafawa.org/

Addressing Asthma from a Public Health Perspective – Washington Department of Health (CDC funded)

Cross-Agency Work Data Measurement/ Reporting

http://www.alaw.org/asthma/washington_asthma_initiative/

Washington

Breathe Easy Washington Program –Washington Department of Health

Public Service and Education

http://www.alaw.org/air_quality/breathe_easy_network/

West Virginia Addressing Asthma from a Public Health Perspective – West Virginia Department of Health and Human Resources

Cross-Agency Work Data Measurement/ Reporting

http://www.wv.gov/offsite.aspx?u=http://www.wvdhhr.org/bph

Wisconsin Addressing Asthma from a Public Health Perspective – Wisconsin Department of Health and Family Services (CDC funded)

Cross-Agency Work Data Measurement/ Reporting

http://www.dhfs.state.wi.us/

Wyoming Wyoming Department of Health General list of asthma resources

Public Service and Education

http://wdh.state.wy.us/asthma/index.asp

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Appendix D: Asthma Measures Table D.1. Inventory and comparison of asthma measures from 12 national sources,1 grouped by type of measure

Type of measure Variants of the measure definitionAge group

Geographic scope Source1

Provider Care (Process)Asthma severity classification done during asthma visit. Children National NICHQPercent of patients with severity assessment for asthma at last visit. All National HRSARate of asthma patients with a documented asthma severity level. All Hospitals JCAHO-

APMIGPercentage of patients who were evaluated during at least one office visit for the frequency (numeric) of daytime and nocturnal asthma symptoms.

All National AMA PCPI (NQF approved)

Severity Assessment - Spirometer measurements used in diagnosis. All Hospitals JCAHOEstablishment of a personal best peak flow measurement. All Hospitals JCAHOPercent of moderate and severe persistent asthma population older than five years who have established a personal best Peak Flow through multiple readings.

All National HRSA

Percent of asthma population that uses a peak flow meter at home. All National MEPSMedications - timing Time since last asthma medication taken. All State/National NAS

Percent of asthma population that takes any prescription medication for asthma.

All National MEPS

Percentage of all patients with mild, moderate, or severe persistent asthma who were prescribed either the preferred long-term control medication (inhaled corticosteroid) or an acceptable alternative treatment.

All National AMA PCPI (NQF approved)

Children National NICHQAll Hospitals JCAHO-

ORYXPercent of asthma patients who use asthma medications. All National HRSARate of medications use for those with a severity assessment of having persistent asthma.

All Hospitals JCAHO-APMIG

Use of systemic corticosteroids in patients with an acute exacerbation. All Hospitals JCAHOPercent of asthma patients who use corticosteroids for long-term control of asthma.

All National HEDIS

Percent of asthma patients who use corticosteroids In inpatient hospital setting.

Children Hospitals JCAHO-ORYX

Medications - frequency Frequency of use of asthma medication in the past 30 days. Adults State/National BRFSSNumber of puffs taken each time. All State/National NASNumber of times per week. All State/National NASPercent of asthma population that has ever use a prescription inhaler. All State/National NASNumber of full canisters used in the past 3 months. All State/National NASUse before exercising. All State/National NASUse any steriod inhalers for asthma. All National MEPSPercent of population that has had a health professional show them how to use an inhaler.

All State/National NAS

Medications - specific type

Percent of asthma population that takes [NAME OF MEDICATION] for their asthma (self assessment).

All State/National NAS

Medications - nebulizer Percent of asthma population that uses a nebulizer with their asthma medication.

All State/National NAS

Medications - OTC Percent of asthma population that has ever used over-the-counter medication for asthma.

All State/National NAS

Percent of asthma population taking asthma medications in pill form. All State/National NASPercent of asthma population taking asthma medications in syrup form. All State/National NASLength of time used fpr each medication. All State/National NAS

Medications - relievers Use of relievers for inpatient childhood asthma. Children Hospitals JCAHO-ORYX

Rate of asthma patients with a documented self-management plan. All Hospitals JCAHO-APMIG

Percent that has taken a class on how to manage asthma. All State/National NASAsthma management plan given to family. Children National NICHQAsthma management plan is discussed with and understood by patient/family. Children Hospitals JCAHO-

ORYXPercent with a printed asthma management plan. All State/National NASPatients with self-management goals during asthma visit. Children National NICHQPercent with self-management goal at last visit. All National HRSA

Severity Assessment

Medications

Use of anti-inflammatory medications to control asthma.

Medications - inhaler

Severity Assessment - peak flow

Medications - corticosteroids

Medications - forms

Documented asthma management plan

Medications - NSAIDS

105

Type of measure Variants of the measure definitionAge group

Geographic scope Source1

Health professional discussed how to better control asthma at time of hospital discharge.

All State/National NAS

Rate of asthma patients who have been educated on asthma triggers and avoidance.

All Hospitals JCAHO-APMIG

Percent of asthma population whose doctor or health professional taught them what to do during an asthma attack.

All State/National NAS

Percent of asthma population whose doctor or health professional taught them how to use a peak flow meter.

All State/National NAS

Percent of asthma population whose doctor or health professional taught them how to recognize early signs or symptoms of an asthma episode.

All State/National NAS

Percent of patients with planned care visits. Children National NICHQAdults State/National BRFSS

All State/National NAS

Doctor visits Time since last talked to a doctor or other health professional about your asthma.

All State/National NAS

Prevention - depression Percent of asthma population screened for depression. All National HRSAPercent of asthma population that has had a flu shot in the past 12 months. Adults State/National BRFSS

One dose of flu vaccine administered in past 15 months. All HRSAPercent of asthma population that has been advised by a health professional to quit smoking.

Adults State/National BRFSS

Rate of smoking cessation advice/counseling with caregivers of children with asthma.

Children Hospitals JCAHO-APMIG

Patient satisfaction Patients satisfaction with asthma care. Children National NICHQPatient/Parent Self-Care (Process)

Percent of asthma population exposed to environmental tobacco smoke. All National HRSAPercent of asthma population evaluated on triggers other than environmental tobacco smoke (dust, mites, cats, dogs, molds/fungi, cockroaches).

All National HRSA

Percent of asthma population whose work environment worsens their asthma. All State/National NAS

Percent of asthma population that has seen cockroaches in the home. All State/National NASPercent of asthma population with mold or musty odor inside home in the past 30 days.

All State/National NAS

Percent of asthma population that uses gas for cooking. All State/National NASPercent of asthma population with unvented gas logs or gas fireplace or gas stove in the home.

All State/National NAS

Percent of asthma population that has smoking in the home in the past week. All State/National NAS

Percent of asthma population that has a wood burning stove in the home. All State/National NASPercent of asthma population that uses air cleaner or purifier inside the home. All State/National NAS

Uses a bathroom fan that vents to the outside. All State/National NASUses a dehumidifier to reduce moisture inside the home. All State/National NASUses an exhaust fan when cooking. All State/National NASPercent of asthma population that has been advised by a health professional to change things in home, school or work to improve asthma.

All State/National NAS

Percent of asthma population that have carpeting or rugs in the bedroom. All State/National NASPercent of asthma population that use mattress covers made for controlling dust mites.

All State/National NAS

Percent of asthma population that use pillow covers made for controlling dust mites.

All State/National NAS

Percent of asthma population with indoor pets. All State/National NASPercent of asthma population that allows pets in the bedroom. All State/National NASPercent of asthma population that washes sheets and pillowcases in hot water.

All State/National NAS

Percent of asthma population that has smoked at least 100 cigarettes in their entire life.

All State/National NAS

Percent of asthma population that now smokes every day, some days or not at all.

All State/National NAS

Prevention smoking cessation counseling

Routine checkups

Behavior - environmental modification

Behavior - smoking

Prevention - flu

Behavior - environmental assessment

Behavior - environmental modification devices

Patient education

Average number of doctor visits for routine checkup for asthma.

106

Type of measure Variants of the measure definitionAge group

Geographic scope Source1

Daily Symptom Burden (Outcome)All State/National NASAdults State/National BRFSS

Number of school/work days missed. All State/National NASAverage number of days lost at work or school in the past 30 days. All National HRSAAverage number of missed school days in the past two months. Children National NICHQPercent of asthma population that experiences limitation in usual activities due to asthma.

All State/National NAS

All State/National NASAdults State/National BRFSSAll National MEPSAll National NHISAll State/National NASAdults State/National BRFSS

Number of asthma attacks in the past 3 months. All State/National NASPercent of people with asthma who have symptoms all the time. All State/National NASTime since last had symptoms of asthma. All State/National NASDuration of most recent asthma attack. All State/National NASPercent of people with asthma who had a longer/shorter recent attack compared to last attack.

All State/National NAS

All State/National NASAll National HRSA

Number of symptom-free days among patients with persistent asthma. Children National NICHQAll State/National NASAdults State/National BRFSS

Avoidable Events All State/National NAS

Adults State/National BRFSSPercent of children with asthma who have had an acute asthma visit in the past two months.

Children National NICHQ

Return to hospital with same asthma diagnosis within 7 days following emergency department visit or observation stay.

Children Hospitals JCAHO-ORYX

Children National NICHQAll National HRSAAll National HRSAAll State/National NASAdults State/National BRFSS

Return to hospital with same asthma diagnosis within 7 days following inpatient discharge.

Children Hospitals JCAHO-ORYX

Return to hospital with same asthma diagnosis with 30 days following inpatient discharge.

Children Hospitals JCAHO-ORYX

Return to hospital with same asthma diagnosis within 30 days following emergency department visit or observation stay.

Children Hospitals JCAHO-ORYX

Average length of stay for discharges from short-stay hospitals for asthma. All National NHDSRisk-adjusted length of stay (LOS) for childhood asthma patients. Children Hospitals JCAHO-

ORYXHave stayed overnight in a hospital due to asthma in the past 12 months. All State/National NAS

Children State/National HCUPAdults State/National HCUP

Percent of children with asthma who have had a hospitalization for asthma in the past six months.

Children National NICHQ

Rate of discharge from short-stay hospitals for asthma. All National NHDS

Number of symptom-free days in the past two weeks.

Activity limitation days

Number of days unable to work or carry out usual activities due to asthma in the past 12 months.

Hospital admissions for asthma per 100,000 population.

Emergency/Urgent care

Symptom - sleep

Symptom - free

Number of emergency department visits in the past 12 months.

Number of days with symptoms of asthma in the past 30 days.Symptom - frequency

Symptom - duration

Number of days when symptoms of asthma make it difficult to stay asleep in the past 30 days.

Number of visits for urgent treatment of worsening asthma symptoms or an asthma episode or attack besides ED or urgent care.

Percent of asthma patients who have had a visit to an ED/Urgent care office for asthma in the past 6 months.

Hospitalizations

Percent of people with asthma who have had an episode of asthma or an asthma attack during the past 12 months.

107

Type of measure Variants of the measure definitionAge group

Geographic scope Source1

Other Dimensions (Prevalence)Children State/National BRFSSAdults State/National BRFSSAll State/National NASAll National MEPSAll National NHIS

Age at diagnosis. Adults State/National BRFSSChildren State/National BRFSSAdults State/National BRFSSAll State/National NASChildren National NICHQAll National HRSA

Percent of asthma population with a parent who has ever been told he or she has asthma.

All State/National NAS

With a sibling who has ever been told he or he has asthma. All State/National NASWith a grandparent who has ever been told he or she has asthma. All State/National NAS

Other Dimensions (Demographic)Highest level of school completed. All State/National NASZip code. All State/National NASTotal combined income in household. All State/National NASHeight. All State/National NASWeight. All State/National NASBirth weight. All State/National NASPercent of asthma population with low birth weight. All State/National NASPercent of asthma population of Hispanic or Latino origin. All State/National NASPercent of asthma population that is white, black, American Indian/Alaska Native, or Asian/Pacific Islander.

All State/National NAS

Percent of asthma population whose household has been without telephone service for 1 week or more in the past 12 months.

All State/National NAS

Percent of people with asthma that have any kind of health care coverage, including health insurance, prepaid plans such as HMOs, or government plans such as Medicare.

All State/National NAS

Percent of people with asthma that did not have any health insurance or coverage.

All State/National NAS

Percent of asthma population that is currently employed/unemployed. All State/National NASPercent of asthma population unable to work for health reasons/disabled. All State/National NAS

1Sources:AMA PCPI = American Medical Association Physician Consortium for Performance Improvement, NQF Ambulatory Care Measures, 2005

Demographics

Prevalence - family history

Prevalence - lifetime

Prevalence - registry

Prevalence - currentPercent of people that has ever been told they have asthma by a doctor or health professional that still have asthma.

Percent of population that has ever been told they have asthma by a doctor or health professional.

Access

NHIS = National Health Interview Survey, CDC, 2001

Employment

Number of asthma patients in the registry.

MEPS = Medical Expenditure Panel Survey, Agency for Healthcare Research and Quality, 2000

NICHQ = National Initiative for Children's Health Care Quality monthly measures for children with asthma for practices participating in initiativeHRSA = Health Resources and Services Administration, Bureau of Primary Health Care, Disparities Collaboratives - Asthma

HCUP = Health Care Utilization Project, Agency for Healthcare Research and Quality, 2004NHDS = National Hospital Discharge Survey, CDC, 2001

JCAHO-APMIG = Joint Commission on Accrediation of Healthcare Organizations, Asthma Performance Measurement Implementation GuideNAS = National Asthma Survey, CDC, 2003 JCAHO-ORYX = JCAHO ORYX initiative on hospital performance for implementation in 2006BRFSS = Behavioral Risk Factor Surveillance Survey, CDC, 2004HEDIS = Healthplan Employer Data and Information Set, National Committee for Quality Assurance, 2003

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Table D.2. State and local asthma measures: four selected quality improvement initiatives

Type of measure Variants of the measure definitionAge group

Geographic scope Source1

Provider Care (Process)Patients with newly diagnosed asthma (moderate or severe) reported to have spirometry as part of their evaluation

All City CASI

Physicians monitoring spirometry or peak flow during office visits

All City CASI

Percent of people with asthma who have had a spirometry measurement.

All State Oregon

Physicians reporting access to spirometry- Spirometer in office- Referral to an asthma specialist who performs spirometry- No access to spirometry

All City CASI

For specialists:Patients with newly diagnosed asthma (moderate or severe) reported to have selected diagnostic techniques as part of their evaluation (percents). Techniques listed: spirometry, chest radiograph, skin testing or radioallergosorbent testing, sinus radiographs, trial of daily peak flow monitoring, sputum examination and stain for eosinophilia

All City CASI

Patients with newly diagnosed asthma (moderate or severe) reported to have daily peak flow as part of their evaluation

All City CASI

Peak flow measurement at ED discharge All City CASIPhysicians monitoring: Techniques listed: spirometry or peak flow during office visits, frequency of wheeze/cough, frequency of beta 2-agonist use, activity levels, frequency of disturbed sleep due to asthma symptoms, loss of work/school days due to asthma, spirometry or peak flow, direct observation of inhaler technique, peak flow diary reviewPercent of physicians using peak flow or PFTI- Acutely symptomatic patient (never, rarely, sometime, often)- Asymptomatic patient (never, rarely, sometimes, often)Patients with newly diagnosed asthma (moderate or severe) reported to have selected diagnostic techniques as part of their evaluation. Techniques listed: spirometry, chest radiograph, skin testing or radioallergosorbent testing, sinus radiographs, trial of daily peak flow monitoring, sputum examination and stain for eosinophilia, sinus radiographs, CT of the sinuses, MRI of the sinuses, nasal speculum examination, rhinolaryngoscopy, upper GI for gastroesophagel reflux disease (GERD), esophogeal pH testing for GERD

All City CASI

Percent of people with persistent asthma who have been seen by a medical practitioner for asthma in the last 12 months

All State Oregon

Percent of members with persistent asthma who have at least one preventive/ambulatory visit with a primary care physician, pulmonologist, or allergist

All State health plans

MQIC

Severity Assessment - spirometry access

Severity Assessment - peak flow et al.

Severity Assessment - spirometry

Doctor visit

109

Type of measure Variants of the measure definitionAge group

Geographic scope Source1

Physicians prescribing inhaled steroid (for patients <5 years old, for patients > 5 years old) for patients with moderate persistent symptoms

All City CASI

Others listed:- Oral beta-agonist (for patients <5 years old, for patients > 5 years old)- Inhaled beta-agonist (for patients <5 years old, for patients > 5 years old)- Theophylline (for patients <5 years old, for patients > 5 years old)- Systemic steroid (for patients <5 years old, for patients > 5 years old)- Inhaled steroid (for patients <5 years old, for patients > 5 years old)- Cromolyn or nedocromil (for patients <5 years old, for patients > 5 years old)

All City CASI

Medications after ED visit

After ED visit percentage of patients given:- Prescription for systemic steroids- Prescription for inhaled steroids/cromolyn- Prescription for antibiotics

All City CASI

Patients with moderate or severe asthma prescribed a corticosteroid inhaler

All City CASI

Patients with asthma for whom any type of metered-dose inhaler is prescribed

All City CASI

During ED visit, formal training in use of metered-dose inhaler, spacer

All City CASI

Percent of people with persistent asthma who have at least one filled prescription for a daily inhaled anti-inflammatory medication

All State Oregon

Percent of people with persistent asthma who use more than one canister of a short-acting inhaled bronchodilator every two months for one year.

All State Oregon

Patients with moderate or severe persistent asthma for whom written treatment plans are routinely developed

All City CASI

Percent of people with asthma who have a written asthma action plan

All State Oregon

Percent of people with asthma who have documentation of asthma education

All State Oregon

Physicians' approach to asthma education.Techniques listed: form education program, informal education delivered by nurse or physician, other, do not provide asthma education

All City CASI

During ED visit:- Formal asthma education by physician or nurse- Written asthma educational material

All City CASI

Percent of people with persistent asthma who have received education about their triggers and how to reduce their exposure to them

All State Oregon

Asthma education

Medications - corticosteroids

Medications - inhaler

Written asthma plans

110

Type of measure Variants of the measure definitionAge group

Geographic scope Source1

NAEPP guidelines

Physicians and NAEPP guidelines- Heard of NAEPP guidelines (yes/no)- Think NAEPP guidelines are useful (extremely useful, somewhat useful, not very useful, no use at all)

All City CASI

Physician’s likelihood of initiating a consultation with an asthma specialist based on the following event or criteria: hospitalization for asthma, an emergency department visit for asthma, multiple medications with continued symptoms, a life-threatening asthma episode, all patients with mild persistent asthma, all patients with moderate persistent asthma, all patients with severe persistent asthma, atypical signs or symptoms, for a diagnosis in child < 3 years old

All City CASI

Percent of people hospitalized for asthma who are seen by an asthma specialist within one month of the hospital discharge date

All State Oregon

Acute exacerbations

Patients who call practice for an acute (not life-threatening) exacerbation are usually:- Told to go to the emergency department- Provided with a same-day office appointment- Scheduled for an appointment within the week- Other

All City CASI

Preventive care - flu vaccine

Percent of people with persistent asthma who have received an influenza immunization in the last 12 months

All State Oregon

Preventive care - allergen testing

Percent of people with persistent non-seasonal asthma who have received allergen testing

All State Oregon

Hospital Care (Process)

Standards of care - hospital

Whether a hospital is using the following hospital-wide:- Currently using NAEPP guidelines- Currently using critical pathways

All City CASI

Standards of care - ICUWhether the hospital is using the following in ICU:- Currently using guidelines- Currently using critical pathways

All City CASI

Standards of care - bedside

Whether care at bedside includes: nebulization, peak flow monitoring, peak flow instruction, evaluation of inhaler technique, inhaled anti-inflammatories, asthma education

All City CASI

Community-based care at hospitals

Percent of hospitals that have:1. Formal asthma education in outpatient setting2. Utilization review for asthma3. Clinical case management program for asthma4. Home visits as part of asthma management5. Community-based asthma screening6. Community-based adult asthma education programs7. Community-based pediatric asthma education programs8. School-based asthma education programs

All City CASI

Emergency Department Care (Process)

Assessment in ED

Whether assessment in the ED includes: 1. PEFR measurement as part of initial assessment2. PEFR measurement to document improvement after treatment3. Pulse oximetry as part of initial assessment4. Pulse oximetry to document improvement after treatment5. Arterial blood gas as part of initial assessment6. Arterial blood gas as part of assessment of severe cases7. Chest radiograph for patients wheezing for the first time8. Chest radiograph for patients with wheezing and fever9. Chest radiograph when diagnosis of asthma is in doubt

All City CASI

Treatment in ED

1. Average time asthma patients spent in ED2. Average time asthma patients spent in ED before disposition3. Percentage of patients receiving:- IV or po steroids used within the first hour- IV or po steroids used at any time during ED care- Theophylline therapy at any time during ED care- Supplemental oxygen at any time during ED care- Treatment for >4 hours4. Percentage of EDs reporting:- Availability of respiratory therapy, both day and night- The first medication given for asthma attack (beta-agonist by nebulizer, beta-agonist by metered-dose inhaler)

All City CASI

Consultation with asthma specialist

111

Type of measure Variants of the measure definitionAge group

Geographic scope Source1

Percent of patients given a specific follow up appointment All City CASI

Percent of people with one emergency department visit for asthma who are seen by a medical practitioner within one month of the emergency department visit date

All State Oregon

Percent of people with two emergency department visits for asthma in 12 months who are seen by an asthma specialist within one month of the most recent emergency department visit

All State Oregon

Managed Care Organizations (Enabling factors)

Management1. MCOs offering an asthma education program2. MCOs offering an asthma case management program3. MCOs offering an asthma disease management program

All City CASI

Benefits

Asthma-specific covered benefits- Medications- Spacer devices- Peak flow meters- Nebulizers- Asthma education- Pillow/mattress covers- Smoking cessation programs- Smoking cessation medication- HEPA filter/cleaner- Dehumidifier- Home assessment

All City CASI

Patient Care - Process Reported as percentage sampled who answered the question correctly1. Asthma cannot be cured2. Vaporizer is good treatment3. Asthma limits exercise4. Need for asymptomatic asthma visits5. Common reason for school absences6. Asthma runs in families7. Asthma is mainly an emotional illness8. Asthma resolves if attacks stop9. Where to go for treatment10. Asthma onset always in childhood11. Signs: shortness of breath12. Signs: chest tightness13. Signs: severe headaches14. Signs: nocturnal cough15. Signs: wheezing with exercise16. Triggers: furry pets17. Triggers: mosquito bites18. Triggers: dampness19. Triggers: cockroaches20. Triggers: poor diet21. Triggers pollen22. Hospitalizations are preventable23. Symptoms are preventable24. Adequacy of OTC medications25. Asthma is a serious disease26. Asthma care is expensive27. See doctor immediately for attack28. Appropriateness of ED for treatment29. Addiction to asthma medicines30. Overprotective mothers and asthmaPercent of people with asthma who has knowledge of asthma medication use and what do in case of an exacerbation.

All State Oregon

Percent of people with asthma who affirm receipt of information about asthma and treatment techniques.

All State Oregon

Percent of people with asthma who report high levels of confidence in understanding and using this information.

All State Oregon

Percent of people with asthma who report behavior consistent with having received and understood this information.

All State Oregon

Asthma knowledge

Follow up after ED visit

112

Type of measure Variants of the measure definitionAge group

Geographic scope Source1

Environmental triggers

Percent of people with persistent asthma with documentation they have been asked at least once about home andoccupational exposures to:- Dust-mites - Animal allergens - Tobacco smoke- Exercise-induced bronchospasm

All State Oregon

Mortality (Outcome)

Mortality ratesMortality rates - By age and gender- By borough

Children City NYCCAI

Avoidable events (Outcome) Emergency/Urgent care Emergency department visits per 1000 members with

asthma. All State health

plansMQIC

Relapse rate Percentage of asthma patients estimated to relapse within 7 days.

All City CASI

Hospitalizations

Hospitalization rates- By age - Comparison of New York City to New York State- Trends 1990-2000 - Distribution by age group- Leading causes of hospitalizations in children 0-14- By age and gender- By income (ZIP code areas)- Distribution by payer - Total charges by payer- Average length of stay- By borough - By neighborhood - By ZIP code - By month and age

Children City NYCCAI

Other dimensions (Prevalence)Self-reported lifetime prevalence for adults age 18 and over- By age - By race/ethnicity - By borough - By neighborhood

Children City NYCCAI

School-based prevalence- By gender - By income (ZIP code areas)- By borough - By neighborhood

Children City NYCCAI

Other dimensions (Behavior)Percent of people with asthma who currently do not smoke cigarettes.

All State Oregon

Percent of non-smokers with asthma who are not exposed to tobacco smoke in the home.

All State Oregon

NYCCAI = New York City Childhood Asthma Initiative (Garg et al, 2003)

Key to Sources:

Prevalence

Smoking

MQIC = Michigan Quality Improvement Initiative Guideline: Management of Persistent Asthma (MQIC, 2005; http://www.mqic.org/meas.htm)

CASI = Chicago Asthma Surveillance Initiative (Weiss KB, Grant EN. The Chicago Asthma Surveillance Initiative: As Oregon = Guide to Improving Asthma Care in Oregon (Oregon Health Division, 2005; http://www.dhs.state.or.us/publichealth/asthma/guideor.cfm)

113

Appendix E: BRFSS Measures, Data, and Benchmarks In 2003, asthma data were collected under the Behavioral Risk Factor Surveillance System for 5 process measures, 7 outcome measures, and 3 prevalence measures. Those measures for adults with asthma include asthma history, routine check ups, doctor visits for asthma, limited activity due to asthma, medications for asthma, asthma symptoms, asthma episodes, emergency department visits, urgent care visits, and sleep difficulty due to asthma. The number of entities reporting varied from 15 to 54 depending on the measure. All 50 States, DC, and 3 U.S. Territories collected data on receipt of influenza vaccination in the past year. In our analysis, adult smokers with asthma were studied to determine the prevalence of smoking and asthma. The BRFSS data are based on telephone surveys developed by the CDC but administered by each State independently. The survey consists of a core set of questions developed by CDC, additional questions developed by the States, and separate, optional modules for States to use. The asthma module, which contains the quality-of-care questions, is optional for State use. More information about the BRFSS data and methods as well as interactive databases with some State and local level asthma data are available at: http://www.cdc.gov/brfss/. Limitations of BRFSS Data Every data source has limitations that can relate to the population represented, methods used to collect the data, definitions, and analytic approaches. These factors affect the estimates generated from a data set. When similar measures from two data sets differ, the cause can usually be traced to the limitations of the data sets. By understanding the limitation of a data set, the strengths and weakness of estimates from the data set can be assessed and the estimates can be used more responsibly. Limitations of BRFSS data include the following:

• BRFSS samples are kept small to minimize survey costs for States. The State BRFSS samples for the year 2001 range from 1,888 to 8,628 respondents (see: http://www.cdc.gov/brfss/technical_infodata/surveydata/2003.htm). Small samples increase the variance of estimates and decrease the size of the difference between two subpopulations that can be detected through the survey responses. In fact, among the asthma measures, the small sample sizes impeded statistical tests of differences, as discussed below.

• The BRFSS survey excludes people without a residential phone and people who are institutionalized. This means that the total population of interest—all people with asthma—will not be represented in the estimates that come from the survey.1 This weakness can be dealt with by carefully discussing BRFSS results in relation to the population it represents.

• BRFSS data are self-reported and reflect the perceptions of respondents. An advantage 1 See: Nelson D, Holtzman D, Bolen J, Stanwyck C, Mack K. Reliability and validity of measures from the behavioral risk factor surveillance system (BRFSS). Sozial un Praventivmedizin. 2001;46(Supp 1):S3-42.

114

of self-reports is that they can reveal information that cannot be obtained from other sources; for example, the receipt of flu vaccinations for people who do not see a doctor during the year. A disadvantage of self-report data is that respondents may have difficulty recalling events, understanding or interpreting questions, or responding truthfully to questions such as about compliance with advice. Furthermore, cultural and language barriers and limited health knowledge can affect the quality of self-reported data.2 These problems may occur with different propensity for different subgroups.

BRFSS data, like most surveys, are limited by budget constraints. Because BRFSS is funded by States which vary considerably in resources allocated to health surveys, these fiscal disparities may affect the quality of the data across States. Such data quality shortcomings can include bias from differential response rates, varying followup periods, and variations in interviewer protocols or skills (for example, extent of probing for answers). Small Sample Size in BRFSS Table E.1 shows that small sample sizes in the BRFSS supplemental asthma survey result in tests that are unreliable. For example for smoking cessation counseling, 15 of 15 reporting States could not be distinguished from the average of the top 2 States (or top10 percent of States). This is partly because smoking cessation counseling is commonly provided across all States (the distribution of percent counseled is narrow), in combination with the small numbers of individuals interviewed in BRFSS. The smaller the difference to be detected, the greater the sample needed. The same issues are apparent for the measure “average number of symptom-free days in the past 2 weeks.” Fourteen of 19 estimates are indistinguishable from the top decile, again a problem of small sample size. By contrast, “flu shots in the past 12 months” is a measure collected from the core BRFSS survey and thus more reliable estimates result. Eleven of 54 entities represent States comparable to the best-in-class average of 5 States in the top 10 percent. The issue of sample size is the main reason that the NHQR, which produces annual estimates, did not include State-level BRFSS data. For State estimates, multiple years of BRFSS should be used. Estimates for individual BRFSS measures by State (including the District of Columbia and U.S. Territories) are presented in Tables E.2-E.16.

2 Ibid.

115

Table E.1. Selected quality measures for asthma by State, District of Columbia, and U.S. Territory, 2003

SE SE SE SE SE SE SE SE SETotal US average 28.3 0.861 82.2 1.613 40.3 0.635 71.1 0.867 10.9 0.903 53.9 1.126 9.7 0.096 28.1 0.888 17.7 0.754Best-in-class average

40.4 2.964 87.9 3.005 53.3 1.547 75.3 1.844 3.3 0.857 63.6 2.486 10.5 0.295 19.4 1.952 12.2 1.464

Worst performing States average

17.4 1.910 75.8 4.069 27.9 1.804 62.1 2.782 18.2 2.028 39.6 2.549 9.0 0.197 35.5 1.863 22.3 2.107

Number of States reporting

19 15 54 19 19 19 19 19 19

Alabama 41.3 3.741Alaska 45.7 † 4.699Arizona 42.5 4.371Arkansas 75.5 † 5.771 46.8 † 3.142California 36.8 2.853Colorado 83.6 † 5.261 43.7 3.079Connecticut 33.2 † 2.691 89.6 † 4.633 45.3 2.648 73.2 † 2.413 16.1 − 2.063 58.8 † 3.292 10.2 † 0.274 30.1 2.579 17.3 2.037Delaware 41.1 † 3.666 39.6 3.519 74.2 † 3.121 8.1 † 2.661 64.4 † 4.001 10.1 † 0.359 33.2 3.517 18.8 2.984District of Columbia 38.9 † 5.035 31.8 4.370 69.2 † 4.504 18.4 5.484 42.0 5.731 10.5 † 0.485 27.6 † 4.500 26.5 4.737Florida 41.6 4.027Georgia 32.7 † 3.166 36.0 3.503 76.1 † 2.895 11.2 3.472 44.0 − 4.194 9.9 † 0.368 26.1 2.778 20.8 2.577Hawaii 29.4 + 3.543 43.4 3.929 69.7 † 3.851 4.1 † 0.881 45.7 4.838 10.4 † 0.372 40.0 − 4.044 13.2 † 2.544Idaho 37.5 2.930Illinois 37.6 2.678Indiana 34.8 † 2.506 40.0 2.521 74.3 † 2.404 8.0 1.881 48.9 2.920 9.1 − 0.287 31.2 2.402 20.4 2.099Iowa 22.0 − 2.661 83.3 † 6.397 40.5 3.244 74.3 † 3.108 6.8 † 3.242 58.2 † 3.880 9.1 0.364 24.0 † 3.003 12.5 † 2.035Kansas 37.4 2.980Kentucky 81.0 † 4.532 37.5 2.522Louisiana 77.1 † 6.601 43.1 3.208Maine 46.4 † 3.691Maryland 24.2 3.103 43.7 3.540 62.9 − 3.712 13.7 3.117 55.0 † 4.787 10.1 † 0.399 28.1 3.259 18.9 † 3.289Massachusetts 43.1 2.312Michigan 29.0 2.888 39.5 3.062 70.9 † 2.990 9.9 † 4.138 61.4 † 3.579 9.3 0.323 26.3 † 2.955 16.7 † 2.511Minnesota 21.3 − 2.987 45.8 † 3.640 71.4 † 3.366 3.0 † 1.112 59.8 † 4.413 9.8 † 0.356 19.1 † 3.154 15.3 † 2.914Mississippi 35.6 3.286Missouri 38.9 3.557Montana 52.9 † 3.677Nebraska 76.3 † 5.698 51.6 † 3.004Nevada 32.7 4.415New Hampshire 21.9 − 2.234 81.2 † 7.810 43.3 2.751 69.2 † 2.739 11.3 2.001 63.0 † 3.163 10.0 † 0.264 25.1 † 2.466 16.8 † 2.271New Jersey 35.8 † 2.088 82.0 † 4.411 37.6 2.004 71.0 † 2.018 18.2 − 2.174 52.0 2.714 10.3 † 0.205 34.0 2.086 17.7 1.771New Mexico 23.8 2.806 46.6 † 3.085 66.5 3.110 14.1 3.056 59.2 † 3.699 9.7 † 0.357 20.0 † 2.336 13.7 † 2.193New York 44.3 2.689North Carolina 84.5 † 5.238 41.3 2.813North Dakota 49.6 † 3.749Ohio 36.9 3.446Oklahoma 30.0 2.300 87.2 † 3.822 44.1 2.472 74.5 † 2.328 10.4 2.599 39.1 − 2.849 9.0 − 0.269 27.8 2.241 21.2 2.349Oregon 40.5 2.904Pennsylvania 40.4 3.289Rhode Island 86.5 † 6.718 46.1 2.938South Carolina 48.7 † 3.000South Dakota 56.0 † 3.603Tennessee 37.1 3.736Texas 25.2 2.783 83.9 † 4.479 38.0 2.848 73.6 † 2.631 8.6 2.149 53.3 3.385 9.6 0.289 31.3 2.994 17.3 † 2.378Utah 16.9 − 3.199 35.6 3.474 61.2 − 4.150 8.8 † 3.809 51.4 4.600 10.0 † 0.401 27.3 † 4.046 12.6 † 2.450Vermont 17.8 − 2.199 36.9 3.055 69.4 † 3.006 3.5 † 1.257 57.4 † 3.782 9.7 † 0.321 19.6 † 2.460 12.1 † 2.096Virginia 29.8 2.947 80.5 † 6.180 39.4 3.391 67.1 2.908 17.2 3.099 55.8 3.562 9.7 † 0.376 28.8 2.817 20.0 2.814Washington 41.7 1.465West Virginia 76.3 † 5.923 45.9 3.225Wisconsin 23.6 2.958 43.5 3.377 65.8 3.533 8.7 † 4.640 56.0 † 4.086 9.9 † 0.364 22.5 † 2.915 15.7 † 2.651Wyoming 55.0 † 3.198Guam 31.3 6.900Puerto Rico 25.6 − 2.482Virgin Islands 18.8 − 6.433Source: BRFSS, CDC, 2003.† Indicates that the State estimate is not significantly different from the best in class average (P>0.05).+ Indicates that the State estimate is statistically better than the national average (P<0.05).− Indicates that the State estimate is statistically worse than the national average (P<0.05).No symbol indicates that State estimate is no different than the national average.

Routine care (in the past 12

months)

Smoking (counseling in

the past 12 months)

Flu shots (in the past 12 months)

Medications (in the past month)

Percent Percent Average Average

Emergency room visits (in the past

12 months)

Percent Percent Percent Percent Percent

Process MeasuresOutcome Measures- Avoidable

health care useOutcome Measures- Daily lifeLimited activity

days (in the past 12 months)

Sleep difficulty (none in the past

month)

Symptom-free (in the past 2

weeks)

Urgent care visits (in the

past 12 months)

116

Table E.2. Lifetime asthma prevalence: Percent of people who were ever told by a health professional that they have asthma by State, District of Columbia, and U. S. Territory, 2003

P value compared to national average

P value compared

to top decile

averageTotal U.S. 257312 11.9 0.121 200657 12.3 0.138 54666 10.03 0.238Top decile average 21099 10.1 0.270 12935 10.2 0.350 3861 7.2 0.452Bottom decile average

38392 14.8 0.232 30176 15.6 0.266 3788 13.3 0.728

Alabama 3339 11.6 0.688 0.626 0.048 2568 11.9 0.796 742 10.31 1.274Alaska 2658 13.3 0.942 0.143 0.001 2357 12.9 0.991 278 15.58 3.063Arizona 3233 12.5 0.845 0.497 0.007 2369 12.4 0.980 833 13.01 1.616Arkansas 4199 11.3 0.569 0.336 0.049 3210 12.3 0.672 961 7.67 0.936California 4475 13.4 − 0.606 0.016 0.000 3580 13.6 0.678 880 12.36 1.268Colorado 4064 12.4 0.621 0.402 0.001 3360 12.4 0.675 681 12.62 1.614Connecticut 5310 12.2 0.512 0.608 0.000 4143 13.3 0.600 1068 7.69 0.918Delaware 4038 11.7 0.668 0.814 0.023 3212 12.2 0.762 797 9.27 1.310District of Columbia 2038 12.7 0.925 0.368 0.006 1661 13.5 1.047 333 9.57 2.071Florida 5034 10.1 ‡ 0.644 0.006 1.000 3552 10.4 0.764 1431 9.19 1.191Georgia 7633 11.8 0.580 0.919 0.007 6138 12.1 0.654 1429 10.36 1.033Hawaii 4332 11.6 0.609 0.595 0.027 3423 12.5 0.710 882 7.67 1.044Idaho 4992 11.7 0.551 0.710 0.010 3944 12 0.629 1020 10.06 1.020Illinois 5264 11.1 0.492 0.129 0.066 4261 11.5 0.552 999 8.81 0.992Indiana 5474 12.0 0.495 0.844 0.001 4344 12.5 0.563 1104 9.83 0.977Iowa 4999 10.3 ‡ 0.556 0.004 0.808 3707 10.9 0.666 1269 7.82 0.857Kansas 4613 11.5 0.582 0.545 0.025 3535 12.4 0.687 1038 7.9 0.907Kentucky 7622 12.6 0.582 0.267 0.000 5690 12.6 0.673 1900 12.48 1.016Louisiana 5072 10.2 ‡ 0.508 0.001 0.848 4064 10.4 0.576 984 9.18 1.019Maine 2384 13.4 0.814 0.068 0.000 1881 14.1 0.955 477 10.55 1.435Maryland 4433 12.3 0.660 0.531 0.002 3587 12.7 0.746 775 10.44 1.317Massachusetts 7569 14.4 − 0.532 0.000 0.000 6059 15.3 0.610 1372 10.67 1.095Michigan 3546 13.6 0.678 0.015 0.000 2703 14.2 0.779 824 10.6 1.173Minnesota 3874 10.5 ‡ 0.596 0.024 0.511 3042 11.2 0.691 832 6.97 0.897Mississippi 4416 10.9 ‡ 0.569 0.089 0.198 3434 11.2 0.651 951 9.4 1.060Missouri 4250 11.9 0.712 0.978 0.019 3130 12.9 0.846 1083 7.91 1.075Montana 4018 11.1 0.670 0.217 0.184 3104 11.2 0.762 882 10.5 1.405Nebraska 4970 10.3 ‡ 0.507 0.002 0.702 3720 10.6 0.588 1225 9.39 0.929Nevada 2969 11.4 0.825 0.573 0.125 2365 12 0.941 599 8.24 1.374New Hampshire 5036 12.9 0.567 0.094 0.000 4041 13.7 0.655 921 8.92 1.004New Jersey 11293 10.9 ‡ 0.365 0.009 0.078 8621 11.5 0.426 2479 8.57 0.653New Mexico 5490 10.5 ‡ 0.495 0.005 0.500 4245 10.6 0.561 1224 9.83 1.005New York 5535 11.7 0.507 0.687 0.006 4350 12.4 0.588 1113 8.39 0.920North Carolina 9446 11.3 0.524 0.281 0.038 7166 11.3 0.600 2210 11.26 0.981North Dakota 3021 10.1 ‡ 0.618 0.004 0.965 2264 10.2 0.716 730 9.66 1.207Ohio 3821 10.8 0.641 0.104 0.287 3049 11.4 0.731 733 8.22 1.309Oklahoma 7624 11.8 0.458 0.816 0.001 5670 12.5 0.538 1918 8.54 0.721Oregon 4010 14.7 − 0.633 0.000 0.000 3112 15.2 0.727 873 12.14 1.181Pennsylvania 3665 11.9 0.616 0.987 0.007 2807 12.2 0.710 827 10.85 1.223Rhode Island 4042 14.4 0.690 0.000 0.000 3161 15.9 0.821 830 8.22 1.017South Carolina 5921 10.2 ‡ 0.473 0.000 0.927 4651 10.1 0.529 1213 10.41 1.077South Dakota 5257 10.7 ‡ 0.572 0.035 0.368 3826 10.7 0.677 1397 10.56 0.959Tennessee 2586 11.8 0.804 0.941 0.040 2064 12.2 0.912 511 9.59 1.411Texas 6022 11.3 0.491 0.206 0.038 4891 11.4 0.550 1083 10.18 1.020Utah 4048 11.3 ‡ 0.692 0.377 0.112 3326 11.4 0.768 703 10.25 1.373Vermont 4243 12.2 0.605 0.638 0.002 3308 13.2 0.707 910 7.14 0.890Virginia 5435 12.1 0.606 0.722 0.002 4379 12.1 0.666 1018 12.88 1.471Washington 18605 13.9 − 0.325 0.000 0.000 14661 14.3 0.368 3920 11.58 0.624West Virginia 3346 11.8 0.623 0.900 0.011 2533 12.1 0.727 801 10.68 1.147Wisconsin 4049 11.0 ‡ 0.623 0.143 0.200 3217 11.3 0.710 802 9.63 1.262Wyoming 3999 11.3 ‡ 0.572 0.266 0.069 3172 11 0.639 801 12.24 1.265Guam 805 10.3 ‡ 1.153 0.170 0.859 733 10.2 1.210 65 12.24 4.061Puerto Rico 4166 20.7 − 0.849 0.000 0.000 3183 22 0.979 978 13.72 1.300Virgin Islands 2051 9.2 ‡ 0.964 0.006 0.385 1735 10 1.098 276 4.92 1.515

DNC=Data Not CollectedDNS=Data Not Sufficient for Release (sample less than 50)Note: Estimates and standard errors have been weighted either to State or Nation as appropriate. Sample size varies across asthma measures because of varying applicability of questions to respondents and refusals.‡ Indicates that the State estimate is not significantly different from the best in class average (P<0.05).+ Indicates that the State estimate is statistically better than the national average (P<0.05).- Indicates that the State estimate is statistically worse than the national average (P<0.05).

Source: Medstat calculations from the Behavioral Risk Factor Surveillance System 2003, Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion.

Standard ErrorPercentSample Size Sample Size Percent

Standard ErrorSample Size Percent

Standard Error

All Adults Adults Age 18-64 Adults Age 65+

117

Table E.3. Current asthma prevalence: Percent of people who were ever told they have asthma who still have asthma by State, District of Columbia, and U.S. Territory, 2003

P value compared to national average

P value compared to top decile

averageTotal U.S. 256651 7.7 0.099 200105 7.8 0.112 54562 7.2 0.200Top decile average 22299 5.9 0.207 17387 5.9 0.233 3960 4.6 0.362Bottom decile average

25719 10.0 0.247 19921 10.3 0.284 4815 10.1 0.577

Alabama 3337 7.5 0.543 0.717 0.006 2567 7.6 0.620 741 7.3 1.108Alaska 2653 9.1 0.826 0.092 0.000 2353 8.7 0.870 277 12.0 2.667Arizona 3223 8.3 0.724 0.412 0.001 2364 8.1 0.834 828 9.4 1.440Arkansas 4189 7.3 0.449 0.384 0.005 3202 7.8 0.523 959 5.4 0.823California 4469 8.4 0.497 0.167 0.000 3575 8.3 0.556 879 8.6 1.059Colorado 4054 8.3 0.504 0.243 0.000 3350 8.4 0.561 681 7.2 1.036Connecticut 5290 8.3 0.430 0.174 0.000 4128 9.0 0.503 1063 5.1 0.779Delaware 4033 7.5 0.531 0.711 0.005 3208 7.8 0.602 796 6.1 1.100District of Columbia 2035 7.8 0.720 0.891 0.011 1658 8.1 0.802 333 6.8 1.796Florida 5020 6.1 ‡ 0.502 0.002 0.713 3540 6.1 0.597 1429 6.0 0.924Georgia 7610 7.0 0.470 0.145 0.032 6119 7.1 0.530 1425 6.5 0.854Hawaii 4328 5.6 ‡ 0.439 0.000 0.537 3419 6.1 0.517 882 3.9 0.687Idaho 4974 7.9 0.468 0.676 0.000 3927 8.0 0.531 1019 7.5 0.914Illinois 5254 7.4 0.396 0.462 0.001 4253 7.4 0.437 997 7.1 0.916Indiana 5461 8.1 0.401 0.333 0.000 4334 8.3 0.453 1101 6.9 0.834Iowa 4996 6.2 ‡ 0.418 0.000 0.520 3704 6.5 0.495 1269 5.3 0.702Kansas 4606 7.5 0.455 0.668 0.001 3530 7.8 0.528 1036 6.2 0.829Kentucky 7600 9.8 0.507 0.000 0.000 5674 9.6 0.582 1894 10.8 0.952Louisiana 5066 6.2 ‡ 0.398 0.000 0.504 4059 6.2 0.446 983 6.6 0.869Maine 2375 9.9 − 0.700 0.002 0.000 1873 10.1 0.815 476 9.0 1.322Maryland 4420 7.8 0.537 0.855 0.001 3575 8.0 0.610 774 7.2 1.038Massachusetts 7548 9.9 − 0.442 0.000 0.000 6041 10.4 0.510 1369 7.7 0.864Michigan 3538 9.3 − 0.566 0.005 0.000 2695 9.4 0.645 824 8.2 1.062Minnesota 3852 6.8 ‡ 0.479 0.066 0.085 3022 7.2 0.552 830 5.0 0.782Mississippi 4405 6.9 ‡ 0.468 0.094 0.051 3424 7.0 0.536 950 6.6 0.901Missouri 4242 8.0 0.582 0.611 0.001 3125 8.6 0.693 1080 5.5 0.865Montana 4009 7.9 0.562 0.726 0.001 3095 7.8 0.628 882 8.6 1.276Nebraska 4958 7.1 0.411 0.156 0.009 3710 7.1 0.470 1223 7.2 0.829Nevada 2954 6.6 ‡ 0.638 0.088 0.297 2351 6.8 0.724 598 5.6 1.155New Hampshire 5014 8.5 0.457 0.087 0.000 4024 8.8 0.522 917 7.0 0.913New Jersey 11257 7.1 0.292 0.052 0.001 8592 7.3 0.337 2473 6.1 0.562New Mexico 5486 6.7 ‡ 0.408 0.017 0.080 4242 6.8 0.461 1223 6.7 0.849New York 5518 7.6 0.413 0.814 0.000 4337 8.1 0.477 1110 5.8 0.768North Carolina 9433 7.1 0.400 0.145 0.008 7156 6.7 0.448 2207 8.6 0.872North Dakota 3016 7.0 0.511 0.179 0.046 2262 6.7 0.580 727 8.0 1.097Ohio 3813 7.1 0.507 0.245 0.028 3043 7.2 0.571 731 6.4 1.120Oklahoma 7607 7.6 0.368 0.793 0.000 5659 7.9 0.432 1912 6.1 0.583Oregon 3999 9.3 − 0.524 0.003 0.000 3103 9.5 0.598 871 8.4 1.015Pennsylvania 3652 8.3 0.533 0.268 0.000 2796 8.3 0.607 825 8.6 1.134Rhode Island 4030 9.6 − 0.549 0.001 0.000 3150 10.4 0.651 830 6.0 0.862South Carolina 5913 6.1 ‡ 0.359 0.000 0.629 4643 5.8 0.387 1213 7.8 0.960South Dakota 5246 7.3 0.491 0.425 0.009 3816 7.1 0.578 1396 8.3 0.859Tennessee 2580 7.9 0.641 0.758 0.003 2058 8.0 0.720 511 7.7 1.309Texas 6009 6.9 0.401 0.053 0.027 4880 6.8 0.446 1081 7.0 0.866Utah 4040 7.4 0.578 0.609 0.015 3322 7.4 0.641 699 6.5 1.130Vermont 4233 8.4 0.523 0.188 0.000 3299 9.0 0.612 909 5.2 0.781Virginia 5411 7.6 0.497 0.844 0.002 4357 7.4 0.543 1016 9.3 1.261Washington 18529 9.1 − 0.266 0.000 0.000 14595 9.2 0.299 3911 8.1 0.541West Virginia 3341 8.1 0.509 0.440 0.000 2529 8.1 0.586 800 8.0 1.005Wisconsin 4039 7.5 0.503 0.696 0.003 3207 7.5 0.564 802 7.4 1.129Wyoming 3986 7.5 0.469 0.676 0.002 3160 7.0 0.513 800 9.7 1.164Guam 803 6.5 ‡ 0.950 0.209 0.537 731 6.6 1.009 65 6.4 2.896Puerto Rico 4166 10.8 − 0.619 0.000 0.000 3183 11.3 0.715 978 8.1 0.985Virgin Islands 2042 4.5 ‡ 0.761 0.000 0.076 1726 4.8 0.873 276 2.4 0.979

DNC=Data Not CollectedDNS=Data Not Sufficient for Release (sample less than 50)Note: Estimates and standard errors have been weighted either to State or Nation as appropriate. Sample size varies across asthma measures because of varying applicability of questions to respondents and refusals.‡ Indicates that the State estimate is not significantly different from the best in class average (P<0.05).+ Indicates that the State estimate is statistically better than the national average (P<0.05).- Indicates that the State estimate is statistically worse than the national average (P<0.05).

Source: Medstat calculations from the Behavioral Risk Factor Surveillance System 2003, Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion.

Sample Size PercentStandard

Error Sample Size PercentStandard

ErrorSample Size Percent Standard

Error

All Adults Adults Age 18-64 Adults Age 65+

118

Table E.4. Age at asthma diagnosis: Percent of adults with asthma who were diagnosed before age 10 by State, District of Columbia, and U.S. Territory, 2003

Sample Size PercentStandard

Error

P value compared to

national average

P value compared to

top decile average

Total U.S. 10695 38.5 0.798Top decile average 1423 47.0 1.872Bottom decile average 1062 31.0 1.702

Alabama N/C N/C N/C -- --Alaska N/C N/C N/C -- --Arizona N/C N/C N/C -- --Arkansas N/C N/C N/C -- --California N/C N/C N/C -- --Colorado N/C N/C N/C -- --Connecticut 612 32.1 ‡ 2.238 0.007 0.699Delaware 461 39.0 3.098 0.883 0.024District of Columbia 227 42.2 4.024 0.370 0.011Florida N/C N/C N/C -- --Georgia 791 46.7 + 2.748 0.004 0.000Hawaii 465 44.7 + 2.880 0.039 0.000Idaho N/C N/C N/C -- --Illinois N/C N/C N/C -- --Indiana 617 35.7 ‡ 2.249 0.235 0.098Iowa 455 42.0 3.014 0.259 0.001Kansas N/C N/C N/C -- --Kentucky N/C N/C N/C -- --Louisiana N/C N/C N/C -- --Maine N/C N/C N/C -- --Maryland 477 33.9 ‡ 2.926 0.129 0.392Massachusetts N/C N/C N/C -- --Michigan 450 29.5 ‡ 2.624 0.001 0.636Minnesota 378 33.5 ‡ 3.054 0.114 0.474Mississippi N/C N/C N/C -- --Missouri N/C N/C N/C -- --Montana N/C N/C N/C -- --Nebraska N/C N/C N/C -- --Nevada N/C N/C N/C -- --New Hampshire 604 32.1 ‡ 2.490 0.015 0.703New Jersey 1130 33.0 ‡ 1.861 0.007 0.428New Mexico 571 33.8 ‡ 2.538 0.076 0.363New York N/C N/C N/C -- --North Carolina N/C N/C N/C -- --North Dakota N/C N/C N/C -- --Ohio N/C N/C N/C -- --Oklahoma 821 45.4 + 2.159 0.003 0.000Oregon N/C N/C N/C -- --Pennsylvania N/C N/C N/C -- --Rhode Island N/C N/C N/C -- --South Carolina N/C N/C N/C -- --South Dakota N/C N/C N/C -- --Tennessee N/C N/C N/C -- --Texas 632 47.4 2.438 0.001 0.000Utah 460 33.7 ‡ 3.133 0.139 0.445Vermont 473 35.8 ‡ 2.686 0.340 0.129Virginia 625 39.7 2.775 0.667 0.007Washington N/C N/C N/C -- --West Virginia N/C N/C N/C -- --Wisconsin 446 33.8 ‡ 2.999 0.129 0.418Wyoming N/C N/C N/C -- --Guam N/C N/C N/C -- --Puerto Rico N/C N/C N/C -- --Virgin Islands N/C N/C N/C -- --

N/C=Data Not CollectedN/S=Data Not Sufficient for Release (sample less than 50)Note: Estimates and standard errors have been weighted either to State or Nation as appropriate.

‡ Indicates that the State estimate is not significantly different from the best in class average (P<0.05).+ Indicates that the State estimate is statistically better than the national average (P<0.05).- Indicates that the State estimate is statistically worse than the national average (P<0.05).

Diagnosed at age < 10 years

Source: Medstat calculations from Behavioral Risk Factor Surveillance System 2003, Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion.

Sample size varies across asthma measures because of varying applicability of questions to respondents and refusals.

119

Table E.5. Urgent care visits: Percent of adults currently with asthma who had at least one urgent care visit for asthma with their provider in the past 12 months by State, District of Columbia and U.S. Territory, 2003

Sample Size PercentStandard

Error

P value compared to national average

P value compared to top decile

average Sample Size PercentStandard

Error Sample Size PercentStandard

ErrorTotal U.S. 7239 28.1 0.888 5923 29.2 1.012 1263 21.9 1.685Top decile average 586 19.4 1.952 0.000 579 19.6 1.843 123 16.2 3.371Bottom decile average

1028 35.5 1.863 828 36.5 2.089 173 33.6 4.503

Alabama N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CAlaska N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CArizona N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CArkansas N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CCalifornia N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CColorado N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CConnecticut 431 30.1 2.579 0.463 0.001 374 30.2 2.744 52 29.1 7.851Delaware 322 33.2 3.517 0.160 0.001 265 33.2 3.811 54 33.2 9.334District of Columbia 153 27.6 ‡ 4.500 0.913 0.095 130 28.8 4.941 N/S N/S N/SFlorida N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CGeorgia 525 26.1 2.778 0.493 0.048 432 25.7 3.060 89 26.6 6.110Hawaii 255 40.0 − 4.044 0.004 0.000 215 40.3 4.430 N/S N/S N/SIdaho N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CIllinois N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CIndiana 447 31.2 2.402 0.226 0.000 372 32.8 2.702 74 20.2 5.224Iowa 299 24.0 ‡ 3.003 0.190 0.199 230 25.0 3.478 68 19.0 5.306Kansas N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CKentucky N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CLouisiana N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CMaine N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CMaryland 320 28.1 3.259 1.000 0.022 260 29.6 3.634 58 18.3 6.791Massachusetts N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CMichigan 321 26.3 ‡ 2.955 0.560 0.051 255 28.0 3.347 63 17.0 4.886Minnesota 256 19.1 ‡ 3.154 0.006 0.936 212 21.1 3.535 N/S N/S N/SMississippi N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CMissouri N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CMontana N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CNebraska N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CNevada N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CNew Hampshire 430 25.1 ‡ 2.466 0.252 0.070 361 26.7 2.762 60 15.3 4.669New Jersey 773 34.0 2.086 0.009 0.000 613 35.1 2.356 149 27.6 4.576New Mexico 368 20.0 ‡ 2.336 0.001 0.844 295 18.6 2.532 73 27.4 5.925New York N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CNorth Carolina N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CNorth Dakota N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/COhio N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/COklahoma 559 27.8 2.241 0.901 0.005 439 26.6 2.459 119 33.8 5.021Oregon N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CPennsylvania N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CRhode Island N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CSouth Carolina N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CSouth Dakota N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CTennessee N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CTexas 401 31.3 2.994 0.306 0.001 325 33.1 3.436 73 21.0 4.874Utah 305 27.3 ‡ 4.046 0.847 0.079 252 27.8 4.434 50 24.9 7.728Vermont 330 19.6 ‡ 2.460 0.001 0.949 284 20.6 2.687 N/S N/S N/SVirginia 425 28.8 2.817 0.813 0.006 344 31.2 3.566 80 18.5 5.164Washington N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CWest Virginia N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CWisconsin 319 22.5 ‡ 2.915 0.066 0.377 265 21.3 3.010 53 28.5 8.202Wyoming N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CGuam N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CPuerto Rico N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CVirgin Islands N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

N/C=Data Not CollectedN/S=Data Not Sufficient for Release (sample less than 50)Note: Estimates and standard errors have been weighted either to State or Nation as appropriate. Sample size varies across asthma measures because of varying applicability of questions to respondents and refusals.‡ Indicates that the State estimate is not significantly different from the best in class average (P<0.05).+ Indicates that the State estimate is statistically better than the national average (P<0.05).- Indicates that the State estimate is statistically worse than the national average (P<0.05).

Source: Medstat calculations from the Behavioral Risk Factor Surveillance System 2003, Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion.

All Adults Adults Age 18-64 Adults age 65 and over

120

Table E.6. Emergency room visits: Percent of adults with asthma who have had at least one visit to the emergency room for asthma in the past 12 months by states, District of Columbia and U.S. Territory, 2003

Sample Size Percent Standard

Error

P value compared to national average

P value compared to top decile

average Sample Size Percent Standard

Error Sample Size PercentStandard

ErrorTotal U.S. 7290 17.7 0.754 5955 18.7 0.854 1281 12.4 1.356Top decile average 635 12.2 1.464 0.001 536 12.1 1.712 127 7.0 2.497Bottom decile average

712 22.3 2.107 482 22.8 2.668 145 22.2 4.637

Alabama N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CAlaska N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CArizona N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CArkansas N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CCalifornia N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CColorado N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CConnecticut 437 17.3 2.037 0.838 0.044 377 17.5 2.170 55 16.8 6.615Delaware 322 18.8 2.984 0.725 0.048 265 21.0 3.412 54 7.8 4.239District of Columbia 155 26.5 4.737 0.067 0.004 131 25.3 4.865 N/S N/S N/SFlorida N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CGeorgia 539 20.8 2.577 0.252 0.004 441 20.2 2.877 94 22.2 5.952Hawaii 258 13.2 ‡ 2.544 0.093 0.722 218 13.2 2.777 N/S N/S N/SIdaho N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CIllinois N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CIndiana 446 20.4 2.099 0.226 0.001 370 21.5 2.338 75 14.5 4.653Iowa 301 12.5 ‡ 2.035 0.016 0.918 230 13.4 2.445 70 8.0 3.550Kansas N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CKentucky N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CLouisiana N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CMaine N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CMaryland 321 18.9 ‡ 3.289 0.729 0.064 261 20.5 3.705 58 8.1 3.470Massachusetts N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CMichigan 324 16.7 ‡ 2.511 0.697 0.123 258 17.1 2.806 63 14.7 5.325Minnesota 257 15.3 ‡ 2.914 0.423 0.344 212 16.0 3.247 N/S N/S N/SMississippi N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CMissouri N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CMontana N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CNebraska N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CNevada N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CNew Hampshire 433 16.8 ‡ 2.271 0.717 0.086 362 16.7 2.493 62 18.8 5.854New Jersey 776 17.7 1.771 0.989 0.017 615 19.1 2.043 150 10.4 3.194New Mexico 370 13.7 ‡ 2.193 0.082 0.580 297 14.2 2.448 73 10.8 4.601New York N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CNorth Carolina N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CNorth Dakota N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/COhio N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/COklahoma 557 21.2 2.349 0.160 0.001 439 21.6 2.662 117 19.0 4.026Oregon N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CPennsylvania N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CRhode Island N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CSouth Carolina N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CSouth Dakota N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CTennessee N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CTexas 403 17.3 ‡ 2.378 0.876 0.067 327 19.5 2.757 73 6.4 3.030Utah 304 12.6 ‡ 2.450 0.046 0.894 250 11.5 2.565 51 22.1 7.392Vermont 334 12.1 ‡ 2.096 0.011 0.957 286 12.7 2.300 N/S N/S N/SVirginia 433 20.0 2.814 0.425 0.014 351 21.9 3.185 81 11.7 3.709Washington N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CWest Virginia N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CWisconsin 320 15.7 ‡ 2.651 0.462 0.251 265 16.2 2.834 53 13.4 7.056Wyoming N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CGuam N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CPuerto Rico N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CVirgin Islands N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

N/C=Data Not CollectedN/S=Data Not Sufficient for Release (sample less than 50)Note: Estimates and standard errors have been weighted either to State or Nation as appropriate. Sample size varies across asthma measures because of varying applicability of questions to respondents and refusals.‡ Indicates that the State estimate is not significantly different from the best in class average (P<0.05).+ Indicates that the State estimate is statistically better than the national average (P<0.05).- Indicates that the State estimate is statistically worse than the national average (P<0.05).

Source: Medstat calculations from the Behavioral Risk Factor Surveillance System 2003, Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion.

All Adults Adults Age 18-64 Adults age 65 and over

121

Table E.7. Asthma attacks/episodes: Percent of adults with asthma who had an asthma episode in the past 12 months by State, District of Columbia and U.S. Territory, 2003

Sample Size

Percent with

Asthma Episode

Standard Error

P value compared to national average

P value compared to top decile

average Sample Size

Percent with

Asthma Episode

Standard Error

Sample Size

Percent with

Asthma Episode

Standard Error

Total U.S. 7292 56.0 0.945 5958 58.9 1.046 1280 39.2 2.078Top decile average 761 49.3 2.402 795 52.4 2.775 108 27.6 5.067Bottom decile average

972 63.4 1.846 773 66.1 2.050 185 52.2 4.023

Alabama N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CAlaska N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CArizona N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CArkansas N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CCalifornia N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CColorado N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CConnecticut 434 54.6 ‡ 2.765 0.633 0.148 375 57.5 2.971 54 31.8 7.329Delaware 321 54.7 ‡ 3.564 0.717 0.212 264 60.5 3.829 54 23.3 7.020District of Columbia 156 54.7 ‡ 4.860 0.792 0.320 131 54.6 5.225 N/S N/S N/SFlorida N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CGeorgia 541 53.3 ‡ 3.519 0.465 0.343 445 53.8 3.898 92 48.5 6.883Hawaii 258 56.2 ‡ 4.014 0.970 0.143 218 58.4 4.377 N/S N/S N/SIdaho N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CIllinois N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CIndiana 447 56.6 2.614 0.829 0.040 371 60.4 2.855 75 34.4 6.047Iowa 300 53.2 ‡ 3.325 0.420 0.340 229 57.4 3.855 70 33.1 6.630Kansas N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CKentucky N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CLouisiana N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CMaine N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CMaryland 317 55.7 ‡ 3.688 0.937 0.146 257 57.8 4.094 58 41.6 7.638Massachusetts N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CMichigan 323 53.1 ‡ 3.210 0.380 0.348 257 55.9 3.560 63 38.4 6.767Minnesota 255 60.1 3.543 0.260 0.011 211 64.6 3.823 N/S N/S N/SMississippi N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CMissouri N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CMontana N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CNebraska N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CNevada N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CNew Hampshire 434 56.0 ‡ 2.816 0.989 0.069 362 56.9 3.097 63 49.3 6.915New Jersey 777 52.3 ‡ 2.187 0.118 0.359 619 55.5 2.440 147 33.8 4.630New Mexico 370 52.6 ‡ 3.261 0.323 0.409 297 54.5 3.642 73 42.7 6.852New York N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CNorth Carolina N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CNorth Dakota N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/COhio N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/COklahoma 567 64.1 − 2.426 0.002 0.000 444 66.0 2.722 122 53.8 4.961Oregon N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CPennsylvania N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CRhode Island N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CSouth Carolina N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CSouth Dakota N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CTennessee N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CTexas 405 62.3 − 2.847 0.036 0.000 329 66.1 3.119 73 41.5 6.376Utah 306 58.0 ‡ 4.165 0.633 0.069 251 60.8 4.576 52 39.5 8.696Vermont 329 51.6 ‡ 3.346 0.209 0.572 283 55.7 3.579 N/S N/S N/SVirginia 432 47.6 ‡ 3.378 0.016 0.674 350 50.6 3.900 81 34.2 7.050Washington N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CWest Virginia N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CWisconsin 320 56.2 ‡ 3.583 0.954 0.109 265 58.2 3.960 53 45.4 8.227Wyoming N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CGuam N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CPuerto Rico N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CVirgin Islands N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

N/C=Data Not CollectedN/S=Data Not Sufficient for Release (sample less than 50)Note: Estimates and standard errors have been weighted either to State or Nation as appropriate. Sample size varies across asthma measures because of varying applicability of questions to respondents and refusals.‡ Indicates that the State estimate is not significantly different from the best in class average (P<0.05).+ Indicates that the State estimate is statistically better than the national average (P<0.05).- Indicates that the State estimate is statistically worse than the national average (P<0.05).

Source: Medstat calculations from the Behavioral Risk Factor Surveillance System 2003, Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion.

Adults- total Adults Age 18-64 Adults age 65 and over

122

Table E.8. Limited activity due to asthma: Average number of days adults with asthma were unable to work or carry out usual activities in the past 12 months by State, District of Columbia and U.S. Territory, 2003

Sample Size

Average Number of

DaysStandard

Error

P value compared to national average

P value compared to top decile

averageSample

Size

Average Number of

DaysStandard

ErrorSample

Size

Average Number of

DaysStandard

ErrorTotal U.S. 7063 10.9 0.903 5814 10.1 0.925 1200 15.8 3.082Top decile average 572 3.3 0.857 0.000 424 2.8 0.555 132 5.7 2.492Bottom decile average 913 18.2 2.028 734 18.5 2.232 128 28.3 8.891

Alabama N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CAlaska N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CArizona N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CArkansas N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CCalifornia N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CColorado N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CConnecticut 432 16.1 − 2.063 0.021 0.000 375 15.7 2.147 53 20.5 7.161Delaware 312 8.1 ‡ 2.661 0.316 0.087 258 6.5 2.340 51 19.2 12.991District of Columbia 153 18.4 5.484 0.179 0.007 130 20.0 6.302 N/S N/S N/SFlorida N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CGeorgia 500 11.2 3.472 0.936 0.027 416 10.7 3.888 80 12.9 5.988Hawaii 253 4.1 ‡ 0.881 0.000 0.537 214 3.7 0.897 N/S N/S N/SIdaho N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CIllinois N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CIndiana 431 8.0 1.881 0.163 0.023 359 8.2 2.107 71 7.0 3.800Iowa 289 6.8 ‡ 3.242 0.219 0.302 227 7.2 3.782 61 4.2 3.109Kansas N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CKentucky N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CLouisiana N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CMaine N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CMaryland 316 13.7 3.117 0.386 0.001 260 14.6 3.506 54 7.3 3.866Massachusetts N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CMichigan 314 9.9 ‡ 4.138 0.811 0.119 253 9.7 4.652 58 11.5 7.315Minnesota 252 3.0 ‡ 1.112 0.000 0.850 210 1.9 0.645 N/S N/S N/SMississippi N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CMissouri N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CMontana N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CNebraska N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CNevada N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CNew Hampshire 416 11.3 2.001 0.864 0.000 351 9.2 1.794 57 27.5 10.076New Jersey 760 18.2 − 2.174 0.002 0.000 604 18.1 2.351 147 19.2 5.902New Mexico 364 14.1 3.056 0.312 0.001 293 11.3 2.460 71 29.0 13.906New York N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CNorth Carolina N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CNorth Dakota N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/COhio N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/COklahoma 543 10.4 2.599 0.861 0.009 427 9.8 2.833 115 14.1 6.921Oregon N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CPennsylvania N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CRhode Island N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CSouth Carolina N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CSouth Dakota N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CTennessee N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CTexas 384 8.6 2.149 0.330 0.021 316 6.8 1.872 65 20.0 9.854Utah 301 8.8 ‡ 3.809 0.598 0.156 250 9.7 4.263 N/S N/S N/SVermont 320 3.5 ‡ 1.257 0.000 0.878 278 3.8 1.384 N/S N/S N/SVirginia 406 17.2 3.099 0.050 0.000 330 17.0 3.345 75 18.5 7.539Washington N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CWest Virginia N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CWisconsin 317 8.7 ‡ 4.640 0.643 0.252 263 4.8 1.891 53 27.2 23.898Wyoming N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CGuam N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CPuerto Rico N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CVirgin Islands N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

N/C=Data Not CollectedN/S=Data Not Sufficient for Release (sample less than 50)Note: Estimates and standard errors have been weighted either to State or Nation as appropriate. Sample size varies across asthma measures because of varying applicability of questions to respondents and refusals.‡ Indicates that the State estimate is not significantly different from the best in class average (P<0.05).+ Indicates that the State estimate is statistically better than the national average (P<0.05).- Indicates that the State estimate is statistically worse than the national average (P<0.05).

Source: Medstat calculations from the Behavioral Risk Factor Surveillance System 2003, Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion.

All Adults Adults Age 18-64 Adults age 65 and over

123

Table E.9. No sleep difficulty due to asthma: Percent of adults with asthma who had no difficulty sleeping due to asthma during the past month by State, District of Columbia and U.S. Territory, 2003

Sample Size Percent

Standard Error

P value compared to national average

P value compared to top decile

averageSample

Size PercentStandard

ErrorSample

Size PercentStandard

ErrorTotal U.S. 5286 53.9 1.126 4331 52.0 1.263 912 64.2 2.300Top decile average 544 63.6 2.486 0.000 457 62.8 2.737 115 72.7 4.436Bottom decile average

528 39.6 2.549 675 40.0 2.793 154 51.8 4.799

Alabama N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CAlaska N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CArizona N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CArkansas N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CCalifornia N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CColorado N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CConnecticut 298 58.8 ‡ 3.292 0.156 0.248 257 58.6 3.542 N/S N/S N/SDelaware 229 64.4 ‡ 4.001 0.012 0.872 191 62.6 4.434 N/S N/S N/SDistrict of Columbia 103 42.0 5.731 0.041 0.001 83 45.9 6.435 N/S N/S N/SFlorida N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CGeorgia 407 44.0 − 4.194 0.023 0.000 338 43.6 4.587 66 46.0 8.124Hawaii 173 45.7 4.838 0.098 0.001 143 43.9 5.545 N/S N/S N/SIdaho N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CIllinois N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CIndiana 362 48.9 2.920 0.110 0.000 305 46.5 3.198 57 63.6 6.898Iowa 240 58.2 ‡ 3.880 0.283 0.245 181 54.9 4.471 59 74.7 6.378Kansas N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CKentucky N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CLouisiana N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CMaine N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CMaryland 208 55.0 ‡ 4.787 0.828 0.109 167 56.8 5.291 N/S N/S N/SMassachusetts N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CMichigan 255 61.4 ‡ 3.579 0.047 0.608 200 60.0 4.016 52 67.5 6.933Minnesota 181 59.8 ‡ 4.413 0.195 0.454 148 57.9 4.895 N/S N/S N/SMississippi N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CMissouri N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CMontana N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CNebraska N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CNevada N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CNew Hampshire 315 63.0 ‡ 3.163 0.007 0.880 266 63.0 3.467 N/S N/S N/SNew Jersey 506 52.0 2.714 0.527 0.002 401 50.4 3.051 96 61.0 5.840New Mexico 258 59.2 ‡ 3.699 0.167 0.329 208 58.9 4.137 50 61.0 8.156New York N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CNorth Carolina N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CNorth Dakota N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/COhio N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/COklahoma 425 39.1 − 2.849 0.000 0.000 337 36.3 3.178 88 56.1 5.778Oregon N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CPennsylvania N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CRhode Island N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CSouth Carolina N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CSouth Dakota N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CTennessee N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CTexas 307 53.3 3.385 0.859 0.014 248 49.5 3.789 56 70.6 6.198Utah 238 51.4 4.600 0.592 0.019 193 49.6 5.250 N/S N/S N/SVermont 246 57.4 ‡ 3.782 0.372 0.172 220 56.1 4.012 N/S N/S N/SVirginia 304 55.8 3.562 0.612 0.072 254 51.9 4.301 N/S N/S N/SWashington N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CWest Virginia N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CWisconsin 231 56.0 ‡ 4.086 0.615 0.113 191 54.7 4.494 N/S N/S N/SWyoming N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CGuam N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CPuerto Rico N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CVirgin Islands N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

N/C=Data Not CollectedN/S=Data Not Sufficient for Release (sample less than 50)Note: Estimates and standard errors have been weighted either to State or Nation as appropriate. Sample size varies across asthma measures because of varying applicability of questions to respondents and refusals.‡ Indicates that the State estimate is not significantly different from the best in class average (P<0.05).+ Indicates that the State estimate is statistically better than the national average (P<0.05).- Indicates that the State estimate is statistically worse than the national average (P<0.05).

Source: Medstat calculations from the Behavioral Risk Factor Surveillance System 2003, Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion.

All Adults Adults Age 18-64 Adults age 65 and over

124

Table E.10. Routine care for asthma: Percent of adults with asthma who had 2 or more planned care visits for asthma during the past 12 months by State, District of Columbia and U.S. Territory, 2003

Sample Size Percent

Standard Error

P value compared to national average

P value compared to top decile

averageSample

Size PercentStandard

ErrorSample

Size PercentStandard

ErrorTotal U.S. 7194 28.3 0.861 5903 26.0 0.946 1238 41.7 2.123Top decile average 473 40.4 2.964 0.000 393 37.0 3.149 197 59.6 4.212Bottom decile average

634 17.4 1.910 535 16.1 2.016 128 34.5 4.544

Alabama N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CAlaska N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CArizona N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CArkansas N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CCalifornia N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CColorado N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CConnecticut 431 33.2 ‡ 2.691 0.082 0.072 372 31.8 2.865 54 40.5 7.918Delaware 320 41.1 ‡ 3.666 0.001 0.888 263 37.8 3.926 54 63.7 8.358District of Columbia 153 38.9 ‡ 5.035 0.039 0.792 130 35.3 5.265 N/S N/S N/SFlorida N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CGeorgia 521 32.7 ‡ 3.166 0.182 0.075 431 30.0 3.404 86 54.7 7.183Hawaii 255 29.4 + 3.543 0.759 0.017 215 28.1 3.844 N/S N/S N/SIdaho N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CIllinois N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CIndiana 441 34.8 ‡ 2.506 0.014 0.150 369 33.4 2.735 71 43.7 6.446Iowa 296 22.0 − 2.661 0.024 0.000 226 19.4 2.947 69 33.6 6.214Kansas N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CKentucky N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CLouisiana N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CMaine N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CMaryland 319 24.2 3.103 0.199 0.000 259 22.3 3.362 58 37.1 7.559Massachusetts N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CMichigan 322 29.0 2.888 0.816 0.006 257 26.7 3.162 62 41.7 6.776Minnesota 255 21.3 − 2.987 0.024 0.000 212 20.7 3.269 N/S N/S N/SMississippi N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CMissouri N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CMontana N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CNebraska N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CNevada N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CNew Hampshire 430 21.9 − 2.234 0.008 0.000 362 20.1 2.370 59 35.6 6.716New Jersey 759 35.8 ‡ 2.088 0.001 0.209 605 31.7 2.263 143 58.0 4.881New Mexico 366 23.8 2.806 0.122 0.000 295 20.9 2.960 71 38.8 7.146New York N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CNorth Carolina N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CNorth Dakota N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/COhio N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/COklahoma 558 30.0 2.300 0.479 0.006 440 28.1 2.511 117 42.5 5.066Oregon N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CPennsylvania N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CRhode Island N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CSouth Carolina N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CSouth Dakota N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CTennessee N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CTexas 397 25.2 2.783 0.284 0.000 325 23.1 3.101 69 38.4 6.386Utah 303 16.9 − 3.199 0.001 0.000 251 15.1 3.401 N/S N/S N/SVermont 331 17.8 − 2.199 0.000 0.000 284 16.9 2.325 N/S N/S N/SVirginia 420 29.8 2.947 0.627 0.011 343 27.8 3.423 76 39.1 7.936Washington N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CWest Virginia N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CWisconsin 317 23.6 2.958 0.125 0.000 264 20.9 2.964 52 36.6 8.337Wyoming N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CGuam N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CPuerto Rico N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CVirgin Islands N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

N/C=Data Not CollectedN/S=Data Not Sufficient for Release (sample less than 50)Note: Estimates and standard errors have been weighted either to State or Nation as appropriate. Sample size varies across asthma measures because of varying applicability of questions to respondents and refusals.‡ Indicates that the State estimate is not significantly different from the best in class average (P<0.05).+ Indicates that the State estimate is statistically better than the national average (P<0.05).- Indicates that the State estimate is statistically worse than the national average (P<0.05).

Source: Medstat calculations from the Behavioral Risk Factor Surveillance System 2003, Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion.

All Adults Adults Age 18-64 Adults age 65 and over

125

Table E.11. Doctors visits for asthma: Percent of adults with asthma who had a physician visit for asthma in the past 12 months by State, District of Columbia and U.S. Territory, 2003

Sample Size Percent

Standard Error

P value compared to national average

P value compared to top decile

averageSample

Size PercentStandard

ErrorSample

Size PercentStandard

ErrorTotal U.S. 7294 61.4 0.953 5958 60.8 1.061 1283 64.7 2.038Top decile average 1212 71.1 1.510 992 69.6 1.692 204 80.1 2.998Bottom decile average

871 56.1 2.719 548 55.3 2.861 131 53.0 4.983

Alabama N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CAlaska N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CArizona N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CArkansas N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CCalifornia N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CColorado N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CConnecticut 435 68.7 ‡ 2.477 0.006 0.402 376 67.5 2.686 54 74.7 6.498Delaware 322 68.4 3.460 0.051 0.476 265 66.0 3.894 54 81.9 5.068District of Columbia 154 66.6 4.593 0.272 0.347 130 64.2 5.051 N/S N/S N/SFlorida N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CGeorgia 538 56.6 3.890 0.232 0.001 442 55.6 4.194 92 62.7 6.819Hawaii 258 63.3 ‡ 3.926 0.647 0.062 218 63.1 4.299 N/S N/S N/SIdaho N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CIllinois N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CIndiana 448 64.9 2.508 0.191 0.034 372 65.1 2.748 75 63.4 6.131Iowa 300 63.7 ‡ 3.500 0.532 0.051 230 62.1 3.953 69 71.4 7.062Kansas N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CKentucky N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CLouisiana N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CMaine N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CMaryland 321 61.5 3.534 0.987 0.012 261 63.4 3.892 58 48.9 7.748Massachusetts N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CMichigan 323 60.8 3.178 0.866 0.004 257 58.6 3.557 63 72.5 5.998Minnesota 256 61.7 3.511 0.929 0.014 212 63.0 3.835 N/S N/S N/SMississippi N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CMissouri N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CMontana N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CNebraska N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CNevada N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CNew Hampshire 433 61.4 2.814 0.997 0.002 362 61.1 3.096 62 63.9 6.988New Jersey 777 72.5 ‡ 1.904 0.000 0.567 616 70.9 2.177 150 79.4 3.655New Mexico 368 56.9 3.281 0.184 0.000 295 55.2 3.651 73 65.8 6.241New York N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CNorth Carolina N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CNorth Dakota N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/COhio N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/COklahoma 570 58.9 2.570 0.355 0.000 446 57.2 2.893 123 68.2 4.480Oregon N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CPennsylvania N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CRhode Island N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CSouth Carolina N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CSouth Dakota N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CTennessee N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CTexas 402 59.4 2.934 0.507 0.000 326 60.2 3.263 73 56.2 6.510Utah 307 57.1 4.097 0.308 0.001 253 55.5 4.512 51 67.6 7.922Vermont 333 55.4 3.335 0.081 0.000 285 55.9 3.602 N/S N/S N/SVirginia 429 58.5 3.515 0.422 0.001 347 57.1 4.026 81 64.5 6.699Washington N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CWest Virginia N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CWisconsin 320 58.1 3.579 0.378 0.001 265 56.6 3.978 54 65.5 7.249Wyoming N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CGuam N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CPuerto Rico N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CVirgin Islands N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

N/C=Data Not CollectedN/S=Data Not Sufficient for Release (sample less than 50)Note: Estimates and standard errors have been weighted either to State or Nation as appropriate. Sample size varies across asthma measures because of varying applicability of questions to respondents and refusals.‡ Indicates that the State estimate is not significantly different from the best in class average (P<0.05).+ Indicates that the State estimate is statistically better than the national average (P<0.05).- Indicates that the State estimate is statistically worse than the national average (P<0.05).

Source: Medstat calculations from the Behavioral Risk Factor Surveillance System 2003, Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion.

All Adults Adults Age 18-64 Adults age 65 and over

126

Table E.12. Medications for asthma: Percent of adults with asthma who took asthma medication in the past month by State, District of Columbia and U.S. Territory, 2003

Sample Size Percent

Standard Error

P value compared to national average

P value compared

to top decile

averageSample

Size PercentStandard

ErrorSample

Size PercentStandard

ErrorTotal U.S. 7202 71.1 0.867 5885 68.9 0.986 1264 84.1 1.463Top decile average 1093 75.3 1.844 0.039 800 75.9 2.024 116 93.6 2.135Bottom decile average

615 62.1 2.782 501 59.5 3.084 164 72.7 4.825

Alabama N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CAlaska N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CArizona N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CArkansas N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CCalifornia N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CColorado N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CConnecticut 425 73.2 ‡ 2.413 0.402 0.499 367 71.4 2.640 53 85.1 5.646Delaware 320 74.2 ‡ 3.121 0.338 0.763 264 71.0 3.560 53 90.9 3.500District of Columbia 155 69.2 ‡ 4.504 0.683 0.212 130 65.5 5.028 N/S N/S N/SFlorida N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CGeorgia 527 76.1 ‡ 2.895 0.096 0.808 433 77.4 3.007 90 70.0 7.409Hawaii 251 69.7 ‡ 3.851 0.730 0.193 214 68.2 4.203 N/S N/S N/SIdaho N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CIllinois N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CIndiana 442 74.3 ‡ 2.404 0.209 0.744 367 74.2 2.625 74 76.1 5.791Iowa 301 74.3 ‡ 3.108 0.318 0.786 230 72.4 3.539 70 83.9 6.690Kansas N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CKentucky N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CLouisiana N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CMaine N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CMaryland 307 62.9 − 3.712 0.031 0.003 248 60.5 4.126 57 79.6 6.330Massachusetts N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CMichigan 322 70.9 ‡ 2.990 0.952 0.212 256 66.6 3.403 63 95.9 2.596Minnesota 254 71.4 ‡ 3.366 0.925 0.313 209 70.1 3.728 N/S N/S N/SMississippi N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CMissouri N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CMontana N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CNebraska N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CNevada N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CNew Hampshire 426 69.2 ‡ 2.739 0.499 0.063 358 66.5 3.052 59 85.5 5.031New Jersey 766 71.0 ‡ 2.018 0.947 0.112 607 67.7 2.329 148 86.3 2.788New Mexico 367 66.5 3.110 0.158 0.015 295 64.3 3.537 72 78.3 5.212New York N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CNorth Carolina N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CNorth Dakota N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/COhio N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/COklahoma 566 74.5 ‡ 2.328 0.167 0.795 444 72.4 2.646 121 87.2 3.047Oregon N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CPennsylvania N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CRhode Island N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CSouth Carolina N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CSouth Dakota N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CTennessee N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CTexas 396 73.6 ‡ 2.631 0.367 0.597 322 71.9 3.001 71 84.4 4.302Utah 308 61.2 − 4.150 0.020 0.002 253 58.5 4.582 52 83.9 5.858Vermont 330 69.4 ‡ 3.006 0.590 0.095 283 68.5 3.258 N/S N/S N/SVirginia 421 67.1 2.908 0.191 0.018 341 63.9 3.647 79 80.7 5.353Washington N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CWest Virginia N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CWisconsin 318 65.8 3.533 0.143 0.017 264 60.7 3.954 53 89.8 4.315Wyoming N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CGuam N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CPuerto Rico N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CVirgin Islands N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

N/C=Data Not CollectedN/S=Data Not Sufficient for Release (sample less than 50)Note: Estimates and standard errors have been weighted either to State or Nation as appropriate. Sample size varies across asthma measures because of varying applicability of questions to respondents and refusals.‡ Indicates that the State estimate is not significantly different from the best in class average (P<0.05).+ Indicates that the State estimate is statistically better than the national average (P<0.05).- Indicates that the State estimate is statistically worse than the national average (P<0.05).

Source: Medstat calculations from the Behavioral Risk Factor Surveillance System 2003, Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion.

All Adults Adults Age 18-64 Adults age 65 and over

127

Table E.13. Asthma symptom-free days: Average number of days adults with asthma were free of asthma symptoms in past 2 weeks by State, District of Columbia and U.S. Territory, 2003

Sample Size Average

Standard Error

P value compared to national average

P value compared to top decile

averageSample

Size AverageStandard

ErrorSample

Size AverageStandard

ErrorTotal U.S. 7135 9.7 0.096 5858 9.9 0.104 1225 8.3 0.248Top decile average 398 10.5 0.295 0.014 339 10.8 0.313 139 9.6 0.522Bottom decile average

999 9.0 0.197 808 9.3 0.212 129 6.3 0.561

Alabama N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CAlaska N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CArizona N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CArkansas N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CCalifornia N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CColorado N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CConnecticut 423 10.2 ‡ 0.274 0.081 0.466 366 10.5 0.278 52 8.4 0.968Delaware 314 10.1 ‡ 0.359 0.323 0.351 262 10.4 0.368 N/S N/S N/SDistrict of Columbia 152 10.5 ‡ 0.485 0.090 0.945 129 11.1 0.513 N/S N/S N/SFlorida N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CGeorgia 521 9.9 ‡ 0.368 0.570 0.216 434 9.9 0.405 83 9.8 0.688Hawaii 246 10.4 ‡ 0.372 0.061 0.866 210 10.6 0.395 N/S N/S N/SIdaho N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CIllinois N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CIndiana 441 9.1 − 0.287 0.046 0.001 368 9.2 0.304 72 8.1 0.798Iowa 297 9.1 0.364 0.115 0.003 228 9.7 0.382 68 5.9 0.797Kansas N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CKentucky N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CLouisiana N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CMaine N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CMaryland 308 10.1 ‡ 0.399 0.352 0.399 250 10.2 0.447 56 9.2 0.805Massachusetts N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CMichigan 318 9.3 0.323 0.182 0.004 254 9.6 0.349 61 6.7 0.790Minnesota 257 9.8 ‡ 0.356 0.755 0.139 212 10.0 0.385 N/S N/S N/SMississippi N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CMissouri N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CMontana N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CNebraska N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CNevada N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CNew Hampshire 418 10.0 ‡ 0.264 0.288 0.205 352 10.4 0.270 58 7.7 0.842New Jersey 757 10.3 ‡ 0.205 0.015 0.487 605 10.6 0.216 141 8.6 0.592New Mexico 363 9.7 ‡ 0.357 0.894 0.067 293 9.9 0.389 70 8.2 0.870New York N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CNorth Carolina N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CNorth Dakota N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/COhio N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/COklahoma 558 9.0 − 0.269 0.014 0.000 440 9.3 0.294 117 7.2 0.619Oregon N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CPennsylvania N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CRhode Island N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CSouth Carolina N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CSouth Dakota N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CTennessee N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CTexas 397 9.6 0.289 0.810 0.034 321 9.8 0.313 73 8.7 0.748Utah 303 10.0 ‡ 0.401 0.540 0.272 248 10.1 0.436 52 8.5 0.994Vermont 327 9.7 ‡ 0.321 0.960 0.061 282 9.6 0.343 N/S N/S N/SVirginia 418 9.7 ‡ 0.376 0.904 0.077 340 9.8 0.404 77 8.9 0.822Washington N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CWest Virginia N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CWisconsin 317 9.9 ‡ 0.364 0.610 0.194 264 10.1 0.386 52 8.9 0.941Wyoming N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CGuam N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CPuerto Rico N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CVirgin Islands N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

N/C=Data Not CollectedN/S=Data Not Sufficient for Release (sample less than 50)Note: Estimates and standard errors have been weighted either to State or Nation as appropriate.

Sample size varies across asthma measures because of varying applicability of questions to respondents and refusals.‡ Indicates that the State estimate is not significantly different from the best in class average (P<0.05).+ Indicates that the State estimate is statistically better than the national average (P<0.05).- Indicates that the State estimate is statistically worse than the national average (P<0.05).

Number of symptom-free days: Symptoms Less than once a week = 13 Free Days 1 or 2 times per week = 11 Free Days More than 2 Times per week = 6 Free Days Everyday, not all the time = 0 Free Days Everyday, all the time = 0 Free Days

Symptoms of asthma include cough, wheezing, shortness of breath, chest tightness and phlegm production when you do not have a cold or respiratory infection.

All Adults Adults age 18-64 Adults age 65 and over

Source: Medstat calculations from the Behavioral Risk Factor Surveillance System 2003, Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion.

128

Table E.14. Asthma symptoms: Percent of adults with asthma who experienced asthma symptoms every day in past 2 weeks by State, District of Columbia, and U.S. Territory, 2003

Sample Size Percent

Standard Error

P value compared to national average

P value compared

to top decile

averageSample

Size PercentStandard

ErrorSample

Size PercentStandard

ErrorTotal U.S. 257659 0.4 0.018 200858 0.4 0.020 54799 0.6 0.045Top decile average 13347 0.9 0.092 5092 0.7 0.141 1795 0.9 0.214Bottom decile average 9032 1.8 0.150 7055 1.6 0.162 1845 2.7 0.443

Alabama N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CAlaska N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CArizona N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CArkansas N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CCalifornia N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CColorado N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CConnecticut 5317 1.2 ‡ 0.168 0.000 0.100 4147 1.2 0.182 1070 1.4 0.436Delaware 4042 1.2 ‡ 0.203 0.000 0.224 3215 1.1 0.208 797 1.8 0.627District of Columbia 2042 0.8 ‡ 0.261 0.108 0.773 1663 0.7 0.293 334 1.6 0.643Florida N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CGeorgia 7651 1.1 ‡ 0.199 0.000 0.331 6152 1.1 0.227 1433 1.2 0.291Hawaii 4339 0.7 ‡ 0.136 0.039 0.186 3429 0.7 0.154 883 0.8 0.301Idaho N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CIllinois N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CIndiana 5481 1.7 − 0.188 0.000 0.000 4348 1.7 0.202 1107 2.1 0.507Iowa 5003 1.4 − 0.184 0.000 0.018 3710 1.2 0.196 1270 2.2 0.485Kansas N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CKentucky N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CLouisiana N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CMaine N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CMaryland 4435 1.3 ‡ 0.235 0.000 0.092 3589 1.3 0.268 775 1.5 0.415Massachusetts N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CMichigan 3551 1.8 − 0.246 0.000 0.000 2707 1.6 0.269 825 3.0 0.636Minnesota 3883 1.1 ‡ 0.185 0.000 0.350 3048 1.0 0.203 835 1.5 0.449Mississippi N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CMissouri N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CMontana N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CNebraska N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CNevada N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CNew Hampshire 5042 1.4 − 0.168 0.000 0.012 4045 1.2 0.175 923 2.2 0.492New Jersey 11305 0.9 ‡ 0.098 0.000 1.000 8630 0.8 0.102 2481 1.6 0.294New Mexico 5494 1.4 0.191 0.000 0.029 4246 1.2 0.209 1227 2.0 0.479New York N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CNorth Carolina N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CNorth Dakota N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/COhio N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/COklahoma 7633 1.6 − 0.165 0.000 0.000 5674 1.5 0.188 1922 2.1 0.343Oregon N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CPennsylvania N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CRhode Island N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CSouth Carolina N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CSouth Dakota N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CTennessee N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CTexas 6035 1.0 ‡ 0.140 0.000 0.477 4899 0.9 0.146 1086 1.9 0.448Utah 4054 1.2 ‡ 0.213 0.000 0.233 3329 1.1 0.230 706 1.7 0.582Vermont 4250 1.4 − 0.207 0.000 0.034 3313 1.5 0.243 912 0.9 0.305Virginia 5442 1.4 − 0.199 0.000 0.034 4382 1.2 0.208 1020 2.5 0.615Washington N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CWest Virginia N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CWisconsin 4054 1.3 ‡ 0.210 0.000 0.068 3219 1.2 0.223 805 1.9 0.589Wyoming N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CGuam N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CPuerto Rico N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CVirgin Islands N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/C

N/C=Data Not CollectedN/S=Data Not Sufficient for Release (sample less than 50)Note: Estimates and standard errors have been weighted either to State or Nation as appropriate.

Sample size varies across asthma measures because of varying applicability of questions to respondents and refusals.‡ Indicates that the State estimate is not significantly different from the best in class average (P<0.05).+ Indicates that the State estimate is statistically better than the national average (P<0.05).- Indicates that the State estimate is statistically worse than the national average (P<0.05).

Symptoms of asthma include cough, wheezing, shortness of breath, chest tightness and phlegm production when you do not have a cold or respiratory infection.

Adults age 18-64All Adults Adults age 65 and over

Source: Medstat calculations from the Behavioral Risk Factor Surveillance System 2003, Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion.

129

Table E.15. Smoking cessation counseling: Percent of adults with asthma who were advised to quit smoking by a health professional by State, District of Columbia, and U.S. Territory, 2003

Sample Size Percent

Standard error

P value compared to national average

P value compared

to top decile

averageSample

Size PercentStandard

errorSample

Size PercentStandard

errorTotal U.S. 1483 82.2 1.613 1316 82.1 1.708 167 84.0 4.374Top decile average 175 87.9 3.005 0.095 161 87.9 3.101 -- -- --Bottom decile average

143 75.8 4.069 128 74.7 4.340 -- -- --

Alabama N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CAlaska N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CArizona N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CArkansas 79 75.5 ‡ 5.771 0.264 0.057 71 74.3 6.164 N/S N/S N/SCalifornia N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CColorado 50 83.6 ‡ 5.261 0.801 0.476 N/S N/S N/S N/S N/S N/SConnecticut 53 89.6 ‡ 4.633 0.134 0.765 51 89.3 4.740 N/S N/S N/SDelaware N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CDistrict of Columbia N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CFlorida N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CGeorgia N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CHawaii N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CIdaho N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CIllinois N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CIndiana N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CIowa 52 83.3 ‡ 6.397 0.868 0.515 N/S N/S N/S N/S N/S N/SKansas N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CKentucky 243 81.0 ‡ 4.532 0.807 0.206 208 80.3 4.955 N/S N/S N/SLouisiana 58 77.1 ‡ 6.601 0.457 0.138 50 76.2 7.182 N/S N/S N/SMaine N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CMaryland N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CMassachusetts N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CMichigan N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CMinnesota N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CMississippi N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CMissouri N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CMontana N/S N/S N/S -- -- N/S N/S N/S N/S N/S N/SNebraska 64 76.3 ‡ 5.698 0.317 0.071 57 75.2 6.064 N/S N/S N/SNevada N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CNew Hampshire 75 81.2 ‡ 7.810 0.896 0.420 68 81.0 8.286 N/S N/S N/SNew Jersey 121 82.0 ‡ 4.411 0.969 0.270 107 81.6 4.689 N/S N/S N/SNew Mexico N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CNew York N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CNorth Carolina 160 84.5 ‡ 5.238 0.676 0.573 140 86.1 5.525 N/S N/S N/SNorth Dakota N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/COhio N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/COklahoma 122 87.2 ‡ 3.822 0.229 0.883 110 87.2 3.983 N/S N/S N/SOregon N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CPennsylvania N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CRhode Island 79 86.5 ‡ 6.718 0.534 0.849 73 85.8 7.047 N/S N/S N/SSouth Carolina N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CSouth Dakota N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CTennessee N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CTexas 71 83.9 ‡ 4.479 0.727 0.454 61 84.0 4.771 N/S N/S N/SUtah N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CVermont N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CVirginia 80 80.5 ‡ 6.180 0.791 0.282 74 78.9 6.615 N/S N/S N/SWashington N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CWest Virginia 89 76.3 ‡ 5.923 0.337 0.081 82 77.2 6.118 N/S N/S N/SWisconsin N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CWyoming N/S N/S N/S -- -- N/S N/S N/S N/S N/S N/SGuam N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CPuerto Rico N/C N/C N/C -- -- N/C N/C N/C N/C N/C N/CVirgin Islands N/S N/S N/S -- -- N/S N/S N/S N/S N/S N/S

N/C=Data Not CollectedN/S=Data Not Sufficient for Release (sample less than 50)Note: Estimates and standard errors have been weighted either to State or Nation as appropriate. Sample size varies across asthma measures because of varying applicability of questions to respondents and refusals.‡ Indicates that the State estimate is not significantly different from the best in class average (P<0.05).+ Indicates that the State estimate is statistically better than the national average (P<0.05).- Indicates that the State estimate is statistically worse than the national average (P<0.05).

Source: Medstat calculations from the Behavioral Risk Factor Surveillance System 2003, Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion.

Current Smokers age 18-64Current Smokers age 65 and

overAdults-Current Smokers

130

Table E.16. Percent of all adults who received flu shots and percent of adults with asthma who received flu shots by State, District of Columbia, and U.S. Territory, 2003

Sample Size Percent

Standard Error

Sample Size Percent

Standard Error

P value compared to national average

P value compared

to top decile

averageSample

Size PercentStandard

ErrorTotal U.S. 257022 33.1 0.169 20807 40.3 0.635 200380 25.6 0.177Top decile average 26217 41.2 0.362 1578 53.3 1.547 0.000 18391 33.3 0.427Bottom decile average

15180 24.2 0.436 979 27.9 1.804 15032 19.7 0.432

Alabama 3330 35.2 0.983 266 41.3 3.741 0.785 0.003 2560 27.4 1.077Alaska 2646 34.2 1.297 221 45.7 ‡ 4.699 0.251 0.126 2343 30.7 1.343Arizona 3228 33.0 1.216 297 42.5 4.371 0.615 0.020 2366 24.8 1.322Arkansas 4195 37.8 0.845 322 46.8 ‡ 3.142 0.042 0.064 3204 29.9 0.919California 4471 30.1 0.804 395 36.8 2.853 0.235 0.000 3577 22.6 0.816Colorado 4061 35.3 0.838 348 43.7 3.079 0.284 0.005 3355 29.4 0.882Connecticut 5305 36.8 0.757 453 45.3 2.648 0.068 0.009 4137 27.9 0.805Delaware 4037 35.0 0.984 323 39.6 3.519 0.837 0.000 3210 27.2 1.036District of Columbia 2038 33.4 1.357 161 31.8 4.370 0.054 0.000 1660 27.3 1.434Florida 5026 31.9 0.995 370 41.6 4.027 0.758 0.006 3546 21.5 1.041Georgia 7631 29.0 0.718 562 36.0 3.503 0.230 0.000 6133 23.1 0.756Hawaii 4323 42.4 0.959 259 43.4 3.929 0.431 0.020 3417 34.8 1.057Idaho 4989 32.7 0.800 413 37.5 2.930 0.353 0.000 3943 25.5 0.851Illinois 5262 28.0 0.692 428 37.6 2.678 0.331 0.000 4260 22.0 0.715Indiana 5461 32.7 0.701 468 40.0 2.521 0.903 0.000 4332 25.7 0.735Iowa 4997 38.4 0.811 308 40.5 3.244 0.955 0.000 3706 28.3 0.866Kansas 4609 35.4 0.811 345 37.4 2.980 0.347 0.000 3532 27.4 0.872Kentucky 7620 32.3 0.811 838 37.5 2.522 0.288 0.000 5687 24.7 0.874Louisiana 5070 32.3 0.759 325 43.1 3.208 0.399 0.004 4059 25.3 0.797Maine 2387 37.6 1.099 235 46.4 ‡ 3.691 0.102 0.086 1885 28.6 1.150Maryland 4430 35.2 0.890 340 43.7 3.540 0.347 0.013 3588 29.8 0.956Massachusetts 7552 34.5 0.704 764 43.1 2.312 0.239 0.000 6043 25.5 0.740Michigan 3547 31.1 0.874 327 39.5 3.062 0.799 0.000 2703 24.1 0.937Minnesota 3867 38.0 0.859 258 45.8 ‡ 3.640 0.138 0.057 3035 29.5 0.912Mississippi 4405 33.1 0.825 310 35.6 3.286 0.161 0.000 3423 25.6 0.881Missouri 4247 35.5 1.030 342 38.9 3.557 0.699 0.000 3128 27.5 1.149Montana 4018 36.8 1.025 314 52.9 ‡ 3.677 0.001 0.926 3104 28.6 1.090Nebraska 4971 38.1 0.770 361 51.6 ‡ 3.004 0.000 0.607 3716 29.8 0.839Nevada 2967 25.6 1.120 198 32.7 4.415 0.088 0.000 2363 19.3 1.132New Hampshire 5033 33.7 0.753 442 43.3 2.751 0.280 0.002 4038 25.6 0.786New Jersey 11266 31.3 0.514 825 37.6 2.004 0.193 0.000 8605 23.4 0.546New Mexico 5482 34.6 0.762 389 46.6 ‡ 3.085 0.046 0.052 4236 26.9 0.819New York 5527 32.9 0.732 439 44.3 2.689 0.150 0.004 4343 25.4 0.771North Carolina 9423 33.9 0.770 734 41.3 2.813 0.719 0.000 7147 27.0 0.848North Dakota 3021 37.4 0.977 221 49.6 ‡ 3.749 0.014 0.365 2265 28.5 1.056Ohio 3817 31.1 0.921 300 36.9 3.446 0.339 0.000 3049 23.0 0.933Oklahoma 7602 40.0 0.650 579 44.1 2.472 0.140 0.002 5653 31.9 0.713Oregon 4004 33.4 0.827 382 40.5 2.904 0.946 0.000 3105 25.7 0.879Pennsylvania 3659 35.0 0.884 306 40.4 3.289 0.985 0.000 2797 27.0 0.952Rhode Island 4038 39.3 0.914 417 46.1 2.938 0.055 0.030 3154 30.6 0.989South Carolina 5910 35.0 0.723 392 48.7 ‡ 3.000 0.006 0.171 4643 27.9 0.770South Dakota 5257 46.0 0.816 370 56.0 ‡ 3.603 0.000 0.495 3824 38.0 0.924Tennessee 2585 36.1 1.084 209 37.1 3.736 0.399 0.000 2062 30.5 1.162Texas 6010 32.5 0.692 428 38.0 2.848 0.431 0.000 4879 26.7 0.735Utah 4044 34.5 0.986 311 35.6 3.474 0.183 0.000 3321 28.9 1.039Vermont 4242 33.9 0.828 342 36.9 3.055 0.282 0.000 3307 25.6 0.872Virginia 5434 34.7 0.893 450 39.4 3.391 0.789 0.000 4378 28.5 0.960Washington 18596 35.4 0.441 1780 41.7 1.465 0.368 0.000 14648 28.5 0.471West Virginia 3342 36.6 0.925 300 45.9 3.225 0.089 0.038 2529 28.2 0.997Wisconsin 4051 36.1 0.911 328 43.5 3.377 0.357 0.008 3216 28.3 0.968Wyoming 3989 36.0 0.841 312 55.0 ‡ 3.198 0.000 0.633 3166 28.8 0.893Guam 800 27.5 1.814 58 31.3 6.900 0.194 0.002 729 24.6 1.845Puerto Rico 4114 20.1 0.802 485 25.6 − 2.482 0.000 0.000 3140 16.1 0.843Virgin Islands 2037 19.6 1.229 77 18.8 − 6.433 0.001 0.000 1723 17.1 1.290Source: Medstat calculations from the Behavioral Risk Factor Surveillance System 2003, Centers for Disease Control and Prevention, National Center for ChroN/C=Data Not CollectedN/S=Data Not Sufficient for Release (sample less than 50)Note: Estimates and standard errors have been weighted either to state or nation as appropriate. Sample size varies across asthma measures because of varying applicability of questions to respondents and refusals.‡ Indicates that the State estimate is not significantly different from the best in class average (P<0.05).+ Indicates that the State estimate is statistically better than the national average (P<0.05).- Indicates that the State estimate is statistically worse than the national average (P<0.05).

All Adults Adults-age 18-64All Adults with Asthma

131

Table E.16. Percent of all adults who received flu shots and percent of adults with asthma who received flu shots by State, District of Columbia and U.S. Territory, 2003 (continued)

Sample Size Percent

Standard Error

Sample Size Percent

Standard Error

Sample Size Percent

Standard Error

Total U.S. 16554 33.5 0.678 54651 69.6 0.373 4108 76.1 1.160Top decile average 1352 44.4 1.654 5211 77.7 0.656 305 88.6 1.727Bottom decile average

796 23.8 1.870 2904 51.8 1.203 335 58.6 3.323

Alabama 215 33.8 4.115 741 70.2 1.820 50 76.4 6.083Alaska 186 38.6 4.947 280 66.5 3.755 N/S N/S N/SArizona 209 33.9 4.849 831 68.9 2.286 85 74.1 7.060Arkansas 264 40.2 3.377 963 71.0 1.608 57 87.5 4.070California 311 28.7 2.955 879 72.5 1.931 83 80.8 4.494Colorado 292 38.9 3.315 683 74.2 1.997 53 78.7 6.370Connecticut 390 39.5 2.791 1068 74.3 1.509 56 84.1 5.496Delaware 266 34.8 3.716 797 70.0 2.089 54 67.6 9.339District of Columbia 135 24.7 4.052 333 63.0 3.137 N/S N/S N/SFlorida 254 28.8 4.440 1429 65.9 1.864 110 83.2 4.527Georgia 460 31.6 3.890 1432 67.0 1.684 98 65.4 6.602Hawaii 219 41.0 4.287 879 76.4 1.815 N/S N/S N/SIdaho 328 31.3 3.129 1018 70.3 1.691 85 72.1 5.639Illinois 355 33.8 2.888 998 62.2 1.807 73 60.2 6.655Indiana 391 33.6 2.693 1103 66.1 1.552 76 76.6 5.324Iowa 233 31.3 3.549 1268 77.5 1.344 74 84.8 4.709Kansas 277 30.4 3.146 1038 70.8 1.546 66 77.1 5.715Kentucky 604 29.5 2.817 1900 69.1 1.496 232 72.0 4.232Louisiana 259 36.6 3.533 987 68.3 1.668 65 74.7 5.909Maine 184 39.3 4.047 476 74.8 2.194 50 80.4 6.153Maryland 275 38.4 3.900 771 68.4 2.119 63 80.5 5.490Massachusetts 641 36.5 2.462 1371 74.9 1.472 112 84.7 3.421Michigan 259 34.3 3.339 825 67.5 1.848 65 70.9 6.101Minnesota 213 40.1 3.931 832 80.3 1.428 N/S N/S N/SMississippi 244 30.4 3.558 951 69.0 1.643 65 60.8 6.845Missouri 271 31.9 3.830 1082 69.9 1.897 69 86.7 5.526Montana 245 46.2 4.166 881 72.8 1.928 68 80.8 5.749Nebraska 273 43.1 3.357 1230 73.6 1.352 87 88.2 3.335Nevada 156 27.8 4.698 599 60.0 2.995 N/S N/S N/SNew Hampshire 368 36.7 2.960 922 73.9 1.606 65 84.8 4.847New Jersey 655 31.3 2.128 2469 67.2 1.136 159 70.6 4.224New Mexico 310 39.7 3.292 1225 72.4 1.416 79 81.7 4.514New York 365 38.6 2.917 1112 68.0 1.730 70 79.8 5.731North Carolina 543 34.0 3.117 2206 68.8 1.457 183 70.8 4.740North Dakota 161 38.7 4.186 729 73.0 1.768 57 87.2 4.410Ohio 247 30.4 3.608 729 68.0 2.159 50 72.7 8.449Oklahoma 450 37.3 2.664 1914 75.8 1.067 125 83.0 3.548Oregon 304 34.4 3.191 874 70.5 1.702 75 71.4 5.879Pennsylvania 237 33.6 3.602 831 69.1 1.751 69 68.4 6.415Rhode Island 351 42.0 3.202 833 76.2 1.672 59 76.3 6.125South Carolina 303 38.9 3.337 1210 69.3 1.532 84 85.2 4.286South Dakota 257 45.8 4.176 1399 77.9 1.216 111 89.8 2.791Tennessee 168 32.8 4.039 512 69.1 2.293 N/S N/S N/STexas 349 31.5 3.049 1082 67.7 1.546 75 75.9 5.286Utah 256 30.7 3.718 704 74.8 2.141 52 77.6 7.450Vermont 291 30.7 3.156 911 74.1 1.539 50 88.0 4.293Virginia 365 32.9 3.777 1017 69.6 1.879 84 67.8 6.722Washington 1438 36.4 1.594 3924 73.4 0.864 338 75.9 3.164West Virginia 231 37.5 3.576 801 69.1 1.778 68 79.1 5.369Wisconsin 270 34.3 3.576 805 72.1 1.842 56 85.1 4.700Wyoming 226 46.2 3.743 797 72.6 1.729 82 87.3 4.200Guam 53 28.2 7.040 64 59.7 6.909 N/S N/S N/SPuerto Rico 385 22.2 2.645 969 40.2 2.081 99 49.5 6.259Virgin Islands 67 18.3 6.791 274 34.9 3.865 N/S N/S N/S

Adults with Asthma age 18-64 Adults Age 65 and overAdults With Asthma age 65 and

over

Appendix F: Other Asthma-Related Data Sources This appendix provides information on national and local data sources for asthma noted in this Resource Guide to further assist States in generating estimates or analyzing factors related to the quality of asthma care. The quality of the data is discussed throughout this section, because State leaders in quality improvement must understand issues that will be raised in the improvement process. Health care providers may argue that the data, due to limitations, do not reflect reality. They may say: “The data are the problem and not the health care system.” Understanding data limitations leads to responsible use of data. For the purposes of this Resource Guide, only data sources that are able to provide information that is nationally representative and available by State are used. Different sources use different methods, definitions, and classifications. Some sources produce estimates by State and some by national population subgroup, such as race/ethnicity, gender, age, and income.

Sources of Asthma Data in the NHQR The asthma data in the NHQR come from two data sources: the Healthcare Cost and Utilization Project (provided to AHRQ by statewide discharge data organizations) and NCQA’s Health Plan Employer Data and Information Set. Healthcare Cost and Utilization Project

HCUP is a public-private partnership sponsored by AHRQ with 37 participating States that covered about 90 percent of U.S. discharges in the United States in 2004. Discharge data from 28 of these States are available online through HCUPnet (http://hcupnet.ahrq.gov/). The participating statewide data organizations (government, hospital association, or other private organization) provide their statewide hospital discharge data to HCUP for reformatting into standardized files. While national asthma estimates from HCUP are included in the NHQR, State-level data are reported in the NHQR only for one special analysis of admissions for asthma.

The following HCUP Partners provided data for the 2004 HCUP Nationwide Inpatient Sample:

Arkansas Department of Health & Human Services Arizona Arizona Department of Health Services California Office of Statewide Health Planning & Development Colorado Colorado Health & Hospital Association Connecticut Integrated Health Information (Chime, Inc.) Florida Agency for Health Care Administration Georgia Hospital Association (GHA) Hawaii Hawaii Health Information Corporation Illinois Illinois Department of Public Health Indiana Hospital & Health Association Iowa Iowa Hospital Association

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Kansas Kansas Hospital Association Kentucky Cabinet for Health and Family Services Maryland Maryland Health Services Cost Review Commission Massachusetts Massachusetts Division of Health Care Finance and Policy Michigan Michigan Health & Hospital Association Minnesota Minnesota Hospital Association Missouri Hospital Industry Data Institute Nebraska Nebraska Hospital Association Nevada Department of Human Resources New Hampshire Department of Health and Human Services New Jersey Department of Health & Senior Services New York New York State Department of Health North Carolina North Carolina Department of Health and Human Services Ohio Ohio Hospital Association Oregon Oregon Association of Hospitals & Health Systems

Rhode Island Rhode Island Department of Health South Carolina South Carolina State Budget & Control Board South Dakota South Dakota Association of Healthcare Organizations Tennessee Tennessee Hospital Association Texas Texas Department of State Health Services Utah Utah Department of Health Vermont Vermont Association of Hospitals and Health Systems Virginia Virginia Health Information Washington Washington State Department of Health West Virginia West Virginia Health Care Authority Wisconsin Wisconsin Department of Health & Family Services

Contact information for these statewide data organizations is available at: http://www.hcup-us.ahrq.gov/partners.jsp?SID. Additional information on HCUP data can be found at: http://www.ahrq.gov/data/hcup/hcup-pkt.htm.

The main limitation of HCUP data (or any administrative billing data) is that the data are collected primarily for the purpose of reimbursement, and what is coded as clinical diagnoses and procedures can be affected by reimbursement incentives.1 Such incentives can encourage or discourage coding of specific types of conditions or treatments. In addition, the data do not include detailed clinical information (e.g., physiologic measures) beyond diagnoses and procedures, which are useful in determining patient severity of illness. Nevertheless, HCUP data can be used for many purposes, provided that the bias of coding is considered and ruled out as inconsequential. Thus, while administrative hospital data can be mined for clues to quality of care, analysts should be alert for whether the data contain incomplete entries or inadequate clinical detail.

1 See: Keating N, Landrum M, Landon B, Ayanian J, Borbas C, Guadagnoli E. Managing chronic illness in managed care settings: Measuring the quality of diabetes care using administrative data: Is there a bias? Health Services Research. 2003;38(6):1529-45.

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AHRQ has developed the Quality Indicators for use with HCUP and other hospital administrative data. These indicators use sophisticated clinical algorithms of inclusions and exclusions to define patients with similar characteristics and then calculate the outcomes of these groups of patients across different settings and populations. The algorithms have been tested, reviewed, and hewn by clinical consensus panels under AHRQ sponsorship. The AHRQ Quality Indicators include the Prevention Quality Indicators, which estimate rates of avoidable hospital admissions, including separate indicators for pediatric and adult asthma admissions, as an indirect measure of the quality of ambulatory asthma care in the United States. As tools for local quality improvement, the AHRQ Quality Indicators can be used as screens for quality problems that call for more in-depth local study; they are not considered definitive measures of local quality of care. As national measures they capture trends in quality as well as coding of diagnoses. National estimates of the asthma Prevention Quality Indicators are part of the 2003 and 2004 NHQR and NHDR; State estimates are in the 2004 NHQR. Additional information on the AHRQ Quality Indicators is available at: http://www.qualityindicators.ahrq.gov/.

Health Plan Employer Data and Information Set HEDIS® collects data from health plans across the country. HEDIS® is a set of standardized performance measures designed to ensure that purchasers and consumers have the information they need to reliably compare the performance of managed health care plans. The performance measures in HEDIS® are related to many significant public health issues such as cancer, heart disease, smoking, asthma and diabetes. HEDIS® also includes a standardized survey of consumers' experiences that evaluates plan performance in areas such as customer service, access to care and claims possessing. HEDIS® is sponsored, supported, and maintained by the National Committee for Quality Assurance. Because HEDIS® data are collected at the health plan level, State estimates cannot be made. To provide regional estimates, each health plan is assigned to the State in which the health plan headquarters are located, but these are not necessarily where the practices are located. HEDIS data are also limited in that they are relevant to care provided only under managed health care plans.

Other Sources of Data on Asthma Care Asthma-related measures from the following sources discussed in this Resource Guide are not yet included in the NHQR. (For detailed information on State-level BRFSS measures not yet included in the NHQR, see Appendix E.) National and Setting-Specific Data Sources

Medical Expenditure Panel Survey MEPS is a family of surveys, including a Household Survey and surveys of related health care providers. Information is collected annually on health care utilization, expenditures, and health insurance coverage. For the most part, MEPS data are collected using computer-assisted, in-person interviews. The asthma component is collected via a separate paper and pencil

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questionnaire distributed to respondents who report that they have been diagnosed with asthma. More information about MEPS data and methods is available at: http://www.meps.ahrq.gov/WhatIsMEPS/Overview.HTM. MEPS reports national rates by national subgroup for the percentages of asthma patients who used prescription asthma medications, inhaled steroids, and peak flowmeters. Other measures of asthma process of care are not captured in this data set. The following table shows MEPS data on these measures for 2000: Asthma medication and peak flowmeter use for patients with asthma by age group, 2000 _____________________________________________________________ Measure Total Under Age 18 age 18 and older Percent who used:

Asthma medication 86.5 91.2 84.3 Inhaled steroids 49.8 42.3 53.5 Peak flowmeter in home 31.3 27.5 33.1

_________________________________________________________________________________ Source: Agency for Healthcare Research and Quality (2003). MEPS Statistical Brief #13, Asthma Treatment: Use of Medications and Devices, 2000. MEPS collects expenditure data from a national sample but does not collect data by State; thus State-level estimates are not available. The following table from MEPS shows total expenses for the category of “COPD, asthma” by site of service: Total expenses, in millions of dollars, for COPD, asthma by site of service, 1996-2002 _____________________________________________________________________________________ Year

Total

Outpatient and office-based

medical provider visits

Hospital

inpatient stays

Emergency room visits

Prescribed medicines

Home health

care

2002 45,262.78 11,923.53 12,464.81 1,642.40 15,150.31 4,081.72 2001 44,404.43 9,825.75 16,324.51 1,612.99 13,327.22 3,313.96 2000 36,487.99 7,225.14 13,929.82 1,119.92 8,750.69 5,460.57 1999 33,651.40 7,115.61 11,982.60 1,197.51 8,239.30 5,116.38 1998 31,707.10 6,820.20 14,489.72 915.37 6,719.09 2,762.73 1997 28,973.39 6,356.35 13,256.91 1,090.62 6,100.09 2,169.43 1996 28,594.88 6,895.89 12,702.23 942.14 5,630.05 2,424.57 _____________________________________________________________________________________________ Source: Agency for Healthcare Research and Quality. MEPS Compendium of Tables-Medical Expenditures by Condition (1996-2002). Total expenses for conditions by site of service: United States, 1997. March 3, 2003. Medical Expenditure Panel Survey Component Data. National Hospital Discharge Survey The NHQR uses the National Hospital Discharge Survey for one outcome measure—estimated annual rate of hospitalizations for asthma. The National Center for Health Statistics at CDC uses a national sample of hospitals and a sample of their discharges to collect administrative hospital records for the NHDS (similar to HCUP). The sample consists of about 270,000 inpatient records

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from about 500 hospitals and is representative of inpatient discharges nationally. Additional information on NHDS data is available at: http://www.cdc.gov/nchs/about/major/hdasd/nhdsdes.htm. The limitation of NHDS data are similar to those for HCUP data (described above) because NHDS also uses discharge records or inpatient claims for reimbursement. In addition, although NHDS is a true probability sample, it has a much smaller size than HCUP. As a result, many subgroup estimates that can be made with HCUP cannot be supported with NHDS data. The NHDS cannot produce State-level estimates. National Asthma Survey The National Asthma Survey, a national sample of households interviewed by phone, was conducted by CDC beginning in 2004. The survey contains questions that can be used to develop measures similar to BRFSS asthma measures in addition to other measures related to processes of asthma care including asthma education, peak flow meter use, spirometer use, demographic information of persons with asthma, and others. Pilot survey data were released in 2005. (Results of pilot tests in four States are available at: http://www.cdc.gov/nchs/about/major/slaits/nas.htm.)

National Health Interview Survey Conducted by CDC’s National Center for Health Statistics, the National Health Interview Survey collects data on asthma prevalence for all ages and for children only. Twelve-month prevalence data were collected for children from 1980 to 1996 and for all ages from 1982 to 1996. Beginning in 1997, the survey asks questions on lifetime diagnosis and 12-month attack prevalence; a question on current prevalence (i.e., “Do you still have asthma?”) was added in 2001. The NHIS also includes questions on the number of school days missed by children and the number of workdays missed by adults due to asthma. National Ambulatory Medical Care Survey/National Hospital Ambulatory Medical Care Survey (NAMCS/NHAMCS) These surveys are conducted annually by CDC’s National Center for Health Statistics. NAMCS surveys office-based physicians who are randomly assigned to a 1-week reporting period. The survey form includes questions on reason for the visit and physician diagnosis as well as whether the patient has various chronic diseases, including asthma, regardless of diagnosis. NHAMCS collects similar data for hospital emergency and outpatient departments over a 4-week reporting period. Recent findings related to asthma from these and other NCHS surveys can be found at: http://www.cdc.gov/nchs/products/pubs/pubd/hestats/asthma/asthma.htm. Health Care Setting-Specific Data Sources The following sources collect asthma care quality data at the level of specific health care settings rather than collecting nationally representative or state level data. Therefore, data from these sources are useful for informing initiatives or policies in the appropriate health care setting but not necessarily for broad statewide programs. However, the availability of these data point to

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important opportunities for collaborations with other health providers and sectors to improve the quality of asthma care.

• The Joint Commission on Accreditation of Healthcare Organizations, an organization that oversees the quality of hospitals and other health care organizations, collects hospital data on disease-specific care, including asthma.

• Health Disparities Collaboratives, learning processes of HRSA’s Bureau of Primary Health Care, are disease specific (diabetes, heart disease, and asthma) and include community health centers across the Nation.

• National Institute for Children’s Healthcare Quality Learning Collaboratives are partnerships and learning networks that collect quality data on care for specific diseases in primary care practices. NICHQ efforts aim to measure and improve quality of care and build structural support for quality improvement.

Local Data Sources Below are summaries of some local data sources that have been developed to assess more closely the processes of asthma care for specific populations in specific geographic areas. (See Appendix D for descriptions of measures from local and other health care setting data sources.) Chicago Asthma Surveillance Initiative (CASI) The goal of CASI is to develop a community-wide surveillance program that characterizes and monitors asthma care in the Chicago area in greater detail than other public health surveillance. To accomplish this, CASI surveyed Chicago-area hospitals, emergency departments, primary care physicians, specialty care physicians, pharmacists, managed care organizations, the general public, and persons or families affected by asthma to learn about asthma care and its outcomes. Seven surveys are included: emergency department, hospital, managed care, primary care physician, specialty care physician, pharmacist, and asthma survey of the general population. The CASI surveys were designed to assist the Chicago Asthma Consortium in setting program priorities and to evaluate the impact of these programs over time. The first promising effect stemming from the CASI surveys was the creation of the Chicago Emergency Department Asthma Collaborative in 1997 in which 28 EDs agreed to participate in a 1-year community-based collaborative aimed at improving ED asthma care.2 Guide to Improving Asthma Care in Oregon The goal of this Oregon Asthma Program guide is to steer efforts to improve asthma management and to define appropriate indicators for monitoring the quality of medical care provided to Oregonians with asthma. The guide establishes nine priority areas including: periodic assessment and monitoring of asthma; spirometry; coordination of care; written asthma action plan; asthma education; pharmacology; influenza immunization; assessment, education, management, and treatment of allergens and irritants; and asthma recommendations for health

2 See: Weiss KB, Grant EN. The Chicago Asthma Surveillance Initiative: a community-based approach to understanding asthma care. Chest. 1999;116:141S-145S.

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systems. The guide was developed through a consensus process and includes population-based goals and indicators. The guide does not address all the care a patient with asthma may need. Rather, it is based on a set of procedures that are measurable for defined populations and therefore lend themselves to systematic monitoring. The guide can be accessed at: http://www.dhs.state.or.us/publichealth/asthma/guideor.cfm. Michigan Quality Improvement Consortium (MQIC) The goal of the Michigan Quality Improvement Consortium is to establish and implement a core set of clinical practice guidelines and performance measures for Michigan health plans. The interventions designed and implemented by each plan to improve consistent delivery of services are at the discretion of individual plans; but guidelines, performance goals, measurement methodology, and performance reporting are standardized. The MQIC asthma guideline recommends provision of specific services at least annually including a written action plan for self-management and education regarding use of peak flowmeter, inhaler, spacer and medication, recognition/treatment of symptoms and when to seek medical attention, identification, and avoidance of triggers and smoking cessation counseling. New York City Childhood Asthma Initiative (NYCCAI)

The New York City Department of Health and Mental Hygiene’s Childhood Asthma Initiative is a public health effort to reduce asthma morbidity among children 0 to 18 years of age. Expected outcomes of the NYCCAI include reductions in hospitalizations, ED visits, and school absences due to asthma and improvements in management of childhood asthma among families. The NYCCAI is building on existing research and educational and clinical efforts, resulting in a coordinated and comprehensive effort to understand, treat, and prevent asthma in New York City.3

3 See: Garg R, Karpati A, Leighton J, Perrin M, Shah M. Asthma Facts, Second Edition. New York City Department of Health and Mental Hygiene, May 2003 (available at: http://nyc.gov/html/doh/pdf/asthma/facts.pdf).

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Appendix G: Benchmarks From the NHQR The NHQR provides a national set of estimates and, often, State estimates that can be used as benchmarks for quality improvement. A benchmark can be a baseline or point from which you start, not necessarily representing a goal or target; or it can be the best current rate, something achievable; or it can represent a consensus of what should be achieved. It is a basis for making comparisons. Several types of benchmarks can be derived from the NHQR: • Theoretic limit benchmark—The theoretic limit refers to the maximum or minimum level

that a measure can take on. For example, 100 percent for positive outcomes or 0 percent for negative, avoidable events. In an ideal world, these would be achievable, but in a world where so many factors are involved in achieving a maximum result, those benchmarks may be unrealistic. Also, some concepts might feasibly come closer to the theoretic limit than others.

• Best-in-class benchmark—The rate for the top State or top tier of States can be used for

what manufacturers call a “best in class” benchmark. (The top tier can be defined as the top 5 or 10 percent of States averaged together.) Using influenza vaccination as an example, the highest rate of flu vaccination for people with diabetes across the States (64 percent) may be assumed to be a feasible goal for States to achieve. However, some may view the top State rate as an impractical target given their population and circumstances. Others may view that goal as inadequate depending on the value of the rate and the state of medical knowledge and practice, and they may view the 100-percent goal as their target. These judgments will vary across States because States face different circumstances and environments. This Resource Guide uses the top 10 percent of States, combined in a simple average, to derive the best-in-class estimate. A simple average, rather than weighted average, was used because the denominators from the BRFSS estimates were not available in the NHQR.

• A national consensus-based goal—Some organizations propose targets that should be

achieved to improve the health status of the overall population and vulnerable subgroups. For example, two decades ago, the Centers for Disease Control and Prevention developed diabetes-related goals for a healthier U.S. population. Each decade those goals are reviewed and reestablished.

• National average—The overall average indicates where the average member of a group

stands. For example, the average of influenza vaccination rates for people with diabetes in States (37 percent according to the BRFSS data source) is the “norm” for States or is the rate

Key Messages on Benchmarks: A benchmark: • Is a point for comparison. • Is a place to start. • May be inadequate or impractical from

different vantage points. Methods matter: They can have a large impact on comparisons.

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for the “average” State. States with rates below the average would prefer to be at or above the average. But the average may not be an indicator of quality health care.

• Regional norm—States may prefer a regional estimate for comparison because they want to

see how they perform compared to medical practice within the region. Given the wide regional variation in U.S. medical practice, regional estimates may be weak goals for regions where practice should change to enhance the health care quality for people with asthma. For this Resource Guide, the regional averages are calculated for the four Census regions, Northeast, Midwest, South, and West. (The averages are simple averages because the denominators for BRFSS estimates were not available from the NHQR.)

• State rate—The State’s own rate may serve as a benchmark for various purposes, such as

tracking changes over time, evaluating the effect of a statewide intervention to improve quality, or reporting the norm for local communities and providers to use as a comparison with their own performance. Concerns noted above about using national or regional averages as goals also apply to State rates. For provider-level estimates, the best-in-class providers may be a better indication of what is achievable and should be used as a goal rather than the State average rate. Severity adjustments are an important issue at the provider level, where populations of patients with varying severity and comorbidity levels are unlikely to be distributed evenly across providers.

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Appendix H: Information on Statistical Significance This section is provided for data analysts who wish to generate other statistics and/or perform statistical tests for other comparisons than those that are provided in the NHQR and NHDR. Comparing State and Average Estimates Using P-Values When comparing an individual State estimate to another estimate, such as the all-State average or the average for the top tier of States, every measure has error associated with it. The error is associated with sampling (size of the sample or sampling methods), accuracy of respondent recall and responses, data entry processes, and many other factors. When comparing estimates it is important to take this error (which can be estimated with statistical assumptions) into account. A common statistic for comparing two rates to determine whether they differ is the t-test based on a normal distribution. The t-test can be compared to a normal distribution with a prespecified level of significance or acceptable error in conclusions about whether or not two statistics come from the same distribution or population. The p-value, a statistic for a normal distribution, can be calculated to determine whether two measures are likely from the same or from different distributions. Statistical significance and magnitude of the difference should be considered together when comparing two estimates. The first check should be: Is the difference statistically different? The second check should be: Are the differences large enough to be meaningful for policy purposes? These questions are addressed below: • Is the difference statistically different? Are the p-values less than 0.05? If so, you can

assume that the underlying distributions come from different populations or experiences. But there are some other considerations. The statistical test of differences is affected by the number of observations from which the measures were generated. For example, if the measures were generated from hundreds of thousands of records then summary measures (such as averages) have less variance and lower p-values, which imply “statistical significance” even when the magnitude of the differences might be tiny. Alternatively, when differences are large and the number of observations is few, the absence of statistical significance might simply mean that the data set does not have enough observations for a powerful test. This happens frequently with the BRFSS measures because the annual sample sizes of the State surveys are small—from about 2,000 to 8,500 observations.

• Are the differences large enough to be meaningful for policy purposes? Because of the

relationship between the statistical test and the number of observations, some judgment must be used to assess the meaning of the differences between State estimates. Thus, in addition to statistical significance it is important to ask the second question: Is the State-to-benchmark difference large enough to warrant efforts to rectify it? A one or two percentage point difference in a measure may not be worth the effort to improve it. A 5 or 10 percentage point difference may mean that a substantial number of State residents are affected by poor health care quality in the State. These are judgments that local experts and stake holders who understand the environment of a State can help make.

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Calculating P-Values Calculating the p-value is straightforward when the standard errors (SEs) of the estimate are provided. For example, standard errors are provided for the national average and for individual States. Thus, the test for statistical significance between those two estimates is straightforward (and provided first). However, calculating another average (say, the top decile average) for which the standard error has not been provided is more complicated. In fact, the top decile comparisons in this work are evaluated for statistical significance because the population denominators were not readily available in time for publication of this Resource Guide. Nevertheless, the method for that calculation is presented below. Calculating the p-value when the relevant standard errors are provided. For an individual State estimate compared to the all-State average, the appropriate standard errors have been provided in the NHQR tables. To assess whether or not a State rate is statistically different from the average, calculate the p-value, as follows. Two-sided t-test: Where:

R1 = a State rate R2 = national rate SE2

1 = square of the standard error of the State rate (or its variance) SE2

2 = square of the standard error of the national rate (or its variance)

If the p value is smaller than 0.05, then a State can conclude, with 95 percent confidence, that the State rate is statistically different from the all-State average rate. The p-value can be calculated using SAS or EXCEL with the following data elements and formula functions:

SAS: p = 2 * (1 - PROBNORM(ABS(t))); EXCEL: p= 2*(1- NORMDIST(ABS(t),0,1,TRUE))

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Calculating the p-value when the relevant standard errors are not provided. The fundamental equation of analysis of variance can be used to calculate p-values for other comparisons. For example, comparing a State rate to the average of the top three States would involve the following. The total sum of squares about the overall three-State mean is the sum of the within-State sum of squared deviations from the State mean and the between-State sum of squared deviations from the three-State pooled mean. The within-State sum of squares is obtained by squaring the State’s standard error and multiplying by the sample size. The between-State sum of squares is obtained by summing the sample-weighted squared difference between the State average and the overall three-State average. The formula is below (note: x**2 = x squared and sqrt(x) = square root of x):

Let n1, n2, and n3 be the sample sizes for each State. Let m1, m2, and m3 be the means for each State. Let s1, s2, and s3 be the standard errors for each State. N = n1 + n2 + n3, is the overall three-State sample size. M = (n1*m1 + n2*m2 + n3*m3) / N, is the overall three-State mean. SS = n1*(n1-1)*s1**2 + n2*(n2-1)*s2**2 + n3*(n3-1)*s3**2 + n1*(m1-M)**2 + n2*(m2-M)**2 + n3*(m3-M)**2 VAR = SS / (N-1) SE = sqrt(VAR), which is the estimated standard error for the three-State mean.

Now suppose you have a mean m0 and standard error s0 from a State and you want to test whether m0 is significantly different from M. The test statistic is:

Z = (m0 – M) / sqrt(SE**2 + s0**2),

which can be compared to 1.96 to test the difference at the 5-percent significance level. Or alternatively the p-value can be calculated as in the previous section.